barthel→alb_cate+ast_cate

libraryの読み込み

pacman::p_load(reader, stringr, missForest, mice, ggplot2, sandwich, tidyverse, readxl, tableone, lubridate, skimr, summarytools, naniar, norm2, lmtest, car, ROCR, pROC, Hmisc, rms, glmnet, ggpubr, ggcorrplot,VIM,stringr,doParallel,gtsummary,rmda)

データの読み込み

data<-read_excel("varix_prediction_meld.xlsx")

年度に基づいてchort列を作成し”develop” “validation”をいれる

# "cohort"という新しい列を作成します。これは、年度に基づいて "develop"または "validation"となります。
data_cohort <- data %>%
  mutate(cohort = ifelse(year %in% 2010:2016, "develop",
                         ifelse(year %in% 2017:2022, "validation", NA)))

classの整理

Barthelは0自立、2寝たきり、1それ以外　にカテゴライズ

Child scoreは0:A,1:B,2:C

df <-
  data_cohort|> 　
  mutate(
        hosp_id=as.integer(hosp_id),
        pt_id=as.integer(pt_id),
        hosp_num=as.integer(hosp_num),
        year=as.integer(year),
        age=as.integer(age),
        sex= factor(sex, levels = c("M", "F")),
        smoke= as.integer(smoke),
        barthel= factor(barthel, levels = c("0", "1", "2")),
        child_num= as.integer(child_num),
        child_score=factor(child_score, levels = c("0", "1", "2")),
        gcs=as.integer(gcs),
        cci_num=as.integer(cci_num),
        pad=factor(pad),
        stroke=factor(stroke),
        dimentia=factor(dimentia),
        ch_lung=factor(ch_lung),
        rheumati=factor(rheumati),
        pept_ulcer=factor(pept_ulcer),
        dm=factor(dm),
        dm_compli=factor(dm_compli),
        paralysis=factor(paralysis),
        malignancy=factor(malignancy),
        meta_tumor=factor(meta_tumor),
        aids=factor(aids),
        eGFR30=factor(eGFR30),
        hd=factor(hd),
        hcc=factor(hcc),
        alcohol=factor(alcohol),
        past_rupture=factor(past_rupture),
        antiplate=factor(antiplate),
        anticoag=factor(anticoag),
        antithro=factor(antithro),
        nsaids=factor(nsaids),
        steroid=factor(steroid),
        beta=factor(beta),
        vaso=factor(vaso),
        map= as.integer(map),
        ffp=factor(ffp),
        pc=factor(pc),
        albner=factor(albner),
        sBP= as.integer(sBP),
        dBP= as.integer(dBP),
        hr=as.integer(hr),
        shock=factor(shock),
        los=as.integer(los),
        cohort=factor(cohort),
        meld=as.integer(meld)
         )

開発コホート・検証コホートに分割

# 新しい列 "cohort" を使用してデータフレームを分割します
df_dev <- df %>%
  filter(cohort == "develop")

df_val <- df %>%
  filter(cohort == "validation")

str(df)

## tibble [980 × 64] (S3: tbl_df/tbl/data.frame)
##  $ hosp_id       : int [1:980] 1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
##  $ pt_id         : int [1:980] 1 2 3 4 5 6 7 8 9 10 ...
##  $ hosp_num      : int [1:980] 1 1 1 1 1 1 1 1 1 1 ...
##  $ year          : int [1:980] 2012 2011 2010 2011 2010 2017 2010 2010 2010 2011 ...
##  $ age           : int [1:980] 50 80 59 44 67 47 65 49 73 69 ...
##  $ sex           : Factor w/ 2 levels "M","F": 1 1 2 1 2 1 1 2 1 2 ...
##  $ bmi           : num [1:980] NA 25.3 NA 14.5 NA ...
##  $ smoke         : int [1:980] 0 0 0 240 0 270 0 NA 1000 0 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": NA 3 1 2 NA NA NA NA 1 NA ...
##  $ child_num     : int [1:980] 11 6 NA 8 NA 11 NA NA 9 15 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 1 NA 2 NA 3 NA NA 2 3 ...
##  $ gcs           : int [1:980] 15 15 15 15 15 15 15 15 15 6 ...
##  $ cci_num       : int [1:980] 4 4 3 4 4 4 3 4 4 4 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 2 1 2 2 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
##  $ map           : int [1:980] 0 2 0 6 2 0 2 0 4 14 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 2 1 2 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
##  $ bt            : num [1:980] 36.4 36.8 35.9 36 36.6 36.4 38.4 37 37 35.5 ...
##  $ sBP           : int [1:980] 78 88 100 69 66 102 84 132 90 52 ...
##  $ dBP           : int [1:980] 48 49 56 44 40 67 46 69 54 37 ...
##  $ hr            : int [1:980] 118 72 110 104 72 83 127 83 114 98 ...
##  $ shock         : Factor w/ 2 levels "0","1": 2 1 2 2 2 1 2 1 2 2 ...
##  $ bil           : num [1:980] 2.2 1.2 3.1 3.4 1.2 7.5 2.4 1.2 2.2 8.7 ...
##  $ ast           : num [1:980] 217 31 60 129 52 112 90 154 55 96 ...
##  $ alt           : num [1:980] 63 22 40 46 36 53 19 109 20 87 ...
##  $ wbc           : num [1:980] 7400 5000 7800 9100 3900 9800 8000 7900 11100 12800 ...
##  $ hb            : num [1:980] 6.9 10.8 9.7 10.7 6.3 12.2 9.8 13.5 5.6 6 ...
##  $ plt           : num [1:980] 115 77 74 162 63 69 93 132 124 168 ...
##  $ tp            : num [1:980] 6.3 5.6 6.4 6.1 5.1 5.7 7.2 7.6 4.9 5.3 ...
##  $ alb           : num [1:980] 2.2 3.2 2.8 2.9 2.8 2.6 3.2 4 2.3 1.2 ...
##  $ eGFR          : num [1:980] 58 57.7 63.7 112.4 123.6 ...
##  $ bun           : num [1:980] 13.2 41.5 26.3 2.9 27.4 13.7 15.8 15.5 27 63.2 ...
##  $ cre           : num [1:980] 1.08 0.96 0.72 0.61 0.38 0.74 0.44 0.4 0.97 2.07 ...
##  $ crp           : num [1:980] 0.92 0.29 0.68 0.29 0.29 0.1 0.96 0.29 NA 2.75 ...
##  $ pt            : num [1:980] 37.8 55 46.7 37.8 74.6 28.5 45.9 49.7 45.9 37.2 ...
##  $ aptt          : num [1:980] 29.4 29 27.2 35.3 30.1 37.8 27.6 32.6 27.5 36 ...
##  $ meld          : int [1:980] 16 11 13 14 6 20 8 6 15 29 ...
##  $ hosp_mortality: num [1:980] 0 0 0 0 0 0 0 0 0 1 ...
##  $ los           : int [1:980] 12 7 0 10 3 6 2 1 8 0 ...
##  $ cohort        : Factor w/ 2 levels "develop","validation": 1 1 1 1 1 2 1 1 1 1 ...

tableone作成

col_continuous = c("age", "bmi","smoke","child_num","gcs","cci_num","map","bt","sBP","dBP","hr","bil","ast","alt","wbc","hb","plt","tp","alb","eGFR","bun","cre","crp","pt","aptt","los","meld")
col_factors = c("sex","barthel","child_score","pad","stroke","dimentia","ch_lung","rheumati","pept_ulcer","dm","dm_compli","paralysis","malignancy","meta_tumor","aids","eGFR30","hd","hcc","alcohol","past_rupture","antiplate","anticoag","antithro","nsaids","steroid","beta", "vaso","ffp","pc", "albner","shock","hosp_mortality", "cohort")


# Create your table
df %>%  
  select(c(col_continuous, col_factors)) %>% 
  CreateTableOne(vars = c(col_continuous, col_factors), strata="cohort",factorVars = col_factors, addOverall = T) -> tableone

## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
##   # Was:
##   data %>% select(col_continuous)
## 
##   # Now:
##   data %>% select(all_of(col_continuous))
## 
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
##   # Was:
##   data %>% select(col_factors)
## 
##   # Now:
##   data %>% select(all_of(col_factors))
## 
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

# Print your table
print(tableone, smd = TRUE, missing = TRUE, test = TRUE, explain = TRUE) 

##                          Stratified by cohort
##                           Overall           develop           validation       
##   n                           980               536               444          
##   age (mean (SD))           60.86 (13.01)     61.23 (13.06)     60.42 (12.96)  
##   bmi (mean (SD))           23.61 (11.49)     22.91 (3.73)      24.38 (16.12)  
##   smoke (mean (SD))        265.31 (483.85)   299.62 (571.82)   226.73 (356.99) 
##   child_num (mean (SD))      8.59 (2.12)       8.77 (2.13)       8.38 (2.09)   
##   gcs (mean (SD))           14.46 (1.87)      14.33 (2.20)      14.61 (1.34)   
##   cci_num (mean (SD))        4.47 (1.32)       4.44 (1.20)       4.50 (1.45)   
##   map (mean (SD))            3.37 (3.96)       3.49 (4.51)       3.21 (3.18)   
##   bt (mean (SD))            36.73 (0.72)      36.65 (0.76)      36.81 (0.65)   
##   sBP (mean (SD))           88.55 (16.32)     87.35 (15.48)     89.95 (17.16)  
##   dBP (mean (SD))           52.78 (12.72)     51.00 (11.90)     54.84 (13.33)  
##   hr (mean (SD))            86.90 (21.06)     85.58 (21.10)     88.43 (20.93)  
##   bil (mean (SD))            2.30 (2.19)       2.20 (2.08)       2.42 (2.32)   
##   ast (mean (SD))           83.87 (100.75)    85.41 (92.54)     82.05 (109.78) 
##   alt (mean (SD))           41.79 (46.84)     43.36 (50.96)     39.93 (41.41)  
##   wbc (mean (SD))         8676.10 (4747.62) 8348.30 (4401.79) 9068.42 (5108.48)
##   hb (mean (SD))             8.72 (2.49)       8.55 (2.46)       8.92 (2.51)   
##   plt (mean (SD))          113.92 (66.44)    110.70 (57.80)    117.76 (75.39)  
##   tp (mean (SD))             6.11 (0.92)       6.09 (0.93)       6.13 (0.92)   
##   alb (mean (SD))            2.85 (0.61)       2.82 (0.59)       2.90 (0.64)   
##   eGFR (mean (SD))          69.80 (31.04)     69.15 (30.82)     70.59 (31.31)  
##   bun (mean (SD))           28.52 (17.92)     28.41 (18.64)     28.64 (17.04)  
##   cre (mean (SD))            1.06 (0.90)       1.07 (0.92)       1.05 (0.89)   
##   crp (mean (SD))            0.84 (1.76)       0.81 (1.67)       0.87 (1.86)   
##   pt (mean (SD))            54.98 (17.67)     51.73 (16.99)     58.78 (17.72)  
##   aptt (mean (SD))          34.08 (17.48)     32.43 (17.30)     36.06 (17.52)  
##   los (mean (SD))           12.37 (14.69)     13.77 (15.28)     10.67 (13.77)  
##   meld (mean (SD))          11.91 (6.07)      12.13 (6.16)      11.66 (5.97)   
##   sex = F (%)                 246 ( 25.1)       131 ( 24.4)       115 ( 25.9)  
##   barthel (%)                                                                  
##      0                        313 ( 38.4)       142 ( 35.9)       171 ( 40.7)  
##      1                        249 ( 30.5)       118 ( 29.8)       131 ( 31.2)  
##      2                        254 ( 31.1)       136 ( 34.3)       118 ( 28.1)  
##   child_score (%)                                                              
##      0                        149 ( 16.9)        66 ( 13.9)        83 ( 20.3)  
##      1                        437 ( 49.5)       231 ( 48.7)       206 ( 50.4)  
##      2                        297 ( 33.6)       177 ( 37.3)       120 ( 29.3)  
##   pad = 1 (%)                   2 (  0.2)         1 (  0.2)         1 (  0.2)  
##   stroke = 1 (%)               21 (  2.1)        10 (  1.9)        11 (  2.5)  
##   dimentia = 1 (%)             10 (  1.0)         4 (  0.7)         6 (  1.4)  
##   ch_lung = 1 (%)              15 (  1.5)        11 (  2.1)         4 (  0.9)  
##   rheumati = 1 (%)              3 (  0.3)         3 (  0.6)         0 (  0.0)  
##   pept_ulcer = 1 (%)          101 ( 10.3)        59 ( 11.0)        42 (  9.5)  
##   dm = 1 (%)                  210 ( 21.4)       106 ( 19.8)       104 ( 23.4)  
##   dm_compli = 1 (%)            13 (  1.3)         6 (  1.1)         7 (  1.6)  
##   paralysis = 0 (%)           980 (100.0)       536 (100.0)       444 (100.0)  
##   malignancy = 1 (%)          115 ( 11.7)        58 ( 10.8)        57 ( 12.8)  
##   meta_tumor = 1 (%)           17 (  1.7)         6 (  1.1)        11 (  2.5)  
##   aids = 0 (%)                980 (100.0)       536 (100.0)       444 (100.0)  
##   eGFR30 = 1 (%)               87 (  9.1)        51 (  9.8)        36 (  8.2)  
##   hd = 1 (%)                   13 (  1.3)         3 (  0.6)        10 (  2.3)  
##   hcc = 1 (%)                 175 ( 17.9)       111 ( 20.7)        64 ( 14.4)  
##   alcohol = 1 (%)             472 ( 48.2)       231 ( 43.1)       241 ( 54.3)  
##   past_rupture = 1 (%)        225 ( 23.0)       104 ( 19.4)       121 ( 27.3)  
##   antiplate = 1 (%)             7 (  0.7)         4 (  0.7)         3 (  0.7)  
##   anticoag = 1 (%)              4 (  0.4)         1 (  0.2)         3 (  0.7)  
##   antithro = 1 (%)             10 (  1.0)         4 (  0.7)         6 (  1.4)  
##   nsaids = 1 (%)                9 (  0.9)         5 (  0.9)         4 (  0.9)  
##   steroid = 1 (%)               5 (  0.5)         2 (  0.4)         3 (  0.7)  
##   beta = 1 (%)                 58 (  5.9)        18 (  3.4)        40 (  9.0)  
##   vaso = 1 (%)                 58 (  5.9)        31 (  5.8)        27 (  6.1)  
##   ffp = 1 (%)                 291 ( 29.7)       137 ( 25.6)       154 ( 34.7)  
##   pc = 1 (%)                   17 (  1.7)         6 (  1.1)        11 (  2.5)  
##   albner = 1 (%)               80 (  8.2)        47 (  8.8)        33 (  7.4)  
##   shock = 1 (%)               409 ( 43.0)       215 ( 42.0)       194 ( 44.1)  
##   hosp_mortality = 1 (%)      118 ( 12.0)        73 ( 13.6)        45 ( 10.1)  
##   cohort = validation (%)     444 ( 45.3)         0 (  0.0)       444 (100.0)  
##                          Stratified by cohort
##                           p      test SMD    Missing
##   n                                                 
##   age (mean (SD))          0.336       0.062  0.0   
##   bmi (mean (SD))          0.060       0.126 11.7   
##   smoke (mean (SD))        0.028       0.153 12.9   
##   child_num (mean (SD))    0.007       0.186 13.2   
##   gcs (mean (SD))          0.023       0.149  0.0   
##   cci_num (mean (SD))      0.476       0.045  0.0   
##   map (mean (SD))          0.273       0.071  0.0   
##   bt (mean (SD))           0.001       0.224  3.8   
##   sBP (mean (SD))          0.014       0.160  2.3   
##   dBP (mean (SD))         <0.001       0.304  2.3   
##   hr (mean (SD))           0.038       0.135  2.9   
##   bil (mean (SD))          0.135       0.097  3.8   
##   ast (mean (SD))          0.608       0.033  2.6   
##   alt (mean (SD))          0.261       0.074  2.6   
##   wbc (mean (SD))          0.019       0.151  2.0   
##   hb (mean (SD))           0.022       0.148  2.0   
##   plt (mean (SD))          0.101       0.105  2.0   
##   tp (mean (SD))           0.517       0.043  7.9   
##   alb (mean (SD))          0.042       0.133  4.7   
##   eGFR (mean (SD))         0.474       0.046  2.1   
##   bun (mean (SD))          0.844       0.013  2.1   
##   cre (mean (SD))          0.820       0.015  2.9   
##   crp (mean (SD))          0.632       0.031  5.3   
##   pt (mean (SD))          <0.001       0.407  6.0   
##   aptt (mean (SD))         0.002       0.208 11.4   
##   los (mean (SD))          0.001       0.213  0.0   
##   meld (mean (SD))         0.251       0.077  8.2   
##   sex = F (%)              0.652       0.034  0.0   
##   barthel (%)              0.140       0.139 16.7   
##      0                                              
##      1                                              
##      2                                              
##   child_score (%)          0.008       0.210  9.9   
##      0                                              
##      1                                              
##      2                                              
##   pad = 1 (%)              1.000       0.009  0.0   
##   stroke = 1 (%)           0.662       0.042  0.0   
##   dimentia = 1 (%)         0.536       0.059  0.0   
##   ch_lung = 1 (%)          0.230       0.096  0.0   
##   rheumati = 1 (%)         0.318       0.106  0.0   
##   pept_ulcer = 1 (%)       0.492       0.051  0.0   
##   dm = 1 (%)               0.191       0.089  0.0   
##   dm_compli = 1 (%)        0.732       0.040  0.0   
##   paralysis = 0 (%)           NA      <0.001  0.0   
##   malignancy = 1 (%)       0.381       0.062  0.0   
##   meta_tumor = 1 (%)       0.169       0.102  0.0   
##   aids = 0 (%)                NA      <0.001  0.0   
##   eGFR30 = 1 (%)           0.478       0.054  2.1   
##   hd = 1 (%)               0.043       0.144  0.0   
##   hcc = 1 (%)              0.013       0.166  0.0   
##   alcohol = 1 (%)          0.001       0.225  0.0   
##   past_rupture = 1 (%)     0.005       0.186  0.0   
##   antiplate = 1 (%)        1.000       0.008  0.0   
##   anticoag = 1 (%)         0.489       0.075  0.0   
##   antithro = 1 (%)         0.536       0.059  0.0   
##   nsaids = 1 (%)           1.000       0.003  0.0   
##   steroid = 1 (%)          0.833       0.042  0.0   
##   beta = 1 (%)            <0.001       0.236  0.0   
##   vaso = 1 (%)             0.952       0.013  0.0   
##   ffp = 1 (%)              0.002       0.200  0.0   
##   pc = 1 (%)               0.169       0.102  0.0   
##   albner = 1 (%)           0.520       0.049  0.0   
##   shock = 1 (%)            0.558       0.042  2.9   
##   hosp_mortality = 1 (%)   0.116       0.108  0.0   
##   cohort = validation (%) <0.001         NaN  0.0

tbl_summaryでmedian+IQRも準備する

# specify your data and variables
tbl_summary(data = df, 
            by = "cohort",
            type = list(gcs ~ "continuous", year ~ "categorical"),
            statistic = all_continuous() ~ "{median} ({p25}, {p75})",
            digits = all_continuous() ~ c(0, 2))

Characteristic	develop, N = 5361	validation, N = 4441
hosp_id	1,011 (1,004.00, 1,024)	1,022 (1,006.00, 1,024)
pt_id	372 (177.75, 589)	492 (244.75, 643)
hosp_num
1	472 (88%)	365 (82%)
2	48 (9.0%)	50 (11%)
3	15 (2.8%)	13 (2.9%)
4	1 (0.2%)	10 (2.3%)
5	0 (0%)	4 (0.9%)
6	0 (0%)	2 (0.5%)
year
2010	76 (14%)	0 (0%)
2011	79 (15%)	0 (0%)
2012	88 (16%)	0 (0%)
2013	70 (13%)	0 (0%)
2014	77 (14%)	0 (0%)
2015	72 (13%)	0 (0%)
2016	74 (14%)	0 (0%)
2017	0 (0%)	64 (14%)
2018	0 (0%)	62 (14%)
2019	0 (0%)	72 (16%)
2020	0 (0%)	82 (18%)
2021	0 (0%)	83 (19%)
2022	0 (0%)	81 (18%)
age	62 (51.75, 70)	60 (50.00, 70)
sex
M	405 (76%)	329 (74%)
F	131 (24%)	115 (26%)
bmi	23 (20.32, 25)	23 (20.59, 26)
Unknown	85	30
smoke	20 (0.00, 440)	0 (0.00, 360)
Unknown	84	42
barthel
0	142 (36%)	171 (41%)
1	118 (30%)	131 (31%)
2	136 (34%)	118 (28%)
Unknown	140	24
child_num	8 (7.00, 10)	8 (7.00, 10)
Unknown	81	48
child_score
0	66 (14%)	83 (20%)
1	231 (49%)	206 (50%)
2	177 (37%)	120 (29%)
Unknown	62	35
gcs	15 (15.00, 15)	15 (15.00, 15)
cci_num	4 (4.00, 5)	4 (4.00, 5)
pad
0	535 (100%)	443 (100%)
1	1 (0.2%)	1 (0.2%)
stroke
0	526 (98%)	433 (98%)
1	10 (1.9%)	11 (2.5%)
dimentia
0	532 (99%)	438 (99%)
1	4 (0.7%)	6 (1.4%)
ch_lung
0	525 (98%)	440 (99%)
1	11 (2.1%)	4 (0.9%)
rheumati
0	533 (99%)	444 (100%)
1	3 (0.6%)	0 (0%)
pept_ulcer
0	477 (89%)	402 (91%)
1	59 (11%)	42 (9.5%)
dm
0	430 (80%)	340 (77%)
1	106 (20%)	104 (23%)
dm_compli
0	530 (99%)	437 (98%)
1	6 (1.1%)	7 (1.6%)
paralysis
0	536 (100%)	444 (100%)
malignancy
0	478 (89%)	387 (87%)
1	58 (11%)	57 (13%)
meta_tumor
0	530 (99%)	433 (98%)
1	6 (1.1%)	11 (2.5%)
aids
0	536 (100%)	444 (100%)
eGFR30
0	471 (90%)	401 (92%)
1	51 (9.8%)	36 (8.2%)
Unknown	14	7
hd
0	533 (99%)	434 (98%)
1	3 (0.6%)	10 (2.3%)
hcc
0	425 (79%)	380 (86%)
1	111 (21%)	64 (14%)
alcohol
0	305 (57%)	203 (46%)
1	231 (43%)	241 (54%)
past_rupture
0	432 (81%)	323 (73%)
1	104 (19%)	121 (27%)
antiplate
0	532 (99%)	441 (99%)
1	4 (0.7%)	3 (0.7%)
anticoag
0	535 (100%)	441 (99%)
1	1 (0.2%)	3 (0.7%)
antithro
0	532 (99%)	438 (99%)
1	4 (0.7%)	6 (1.4%)
nsaids
0	531 (99%)	440 (99%)
1	5 (0.9%)	4 (0.9%)
steroid
0	534 (100%)	441 (99%)
1	2 (0.4%)	3 (0.7%)
beta
0	518 (97%)	404 (91%)
1	18 (3.4%)	40 (9.0%)
vaso
0	505 (94%)	417 (94%)
1	31 (5.8%)	27 (6.1%)
map	4 (0.00, 4)	4 (0.00, 4)
ffp
0	399 (74%)	290 (65%)
1	137 (26%)	154 (35%)
pc
0	530 (99%)	433 (98%)
1	6 (1.1%)	11 (2.5%)
albner
0	489 (91%)	411 (93%)
1	47 (8.8%)	33 (7.4%)
bt	37 (36.30, 37)	37 (36.50, 37)
Unknown	32	5
sBP	89 (79.00, 96)	91 (80.00, 100)
Unknown	22	1
dBP	51 (44.00, 58)	54 (46.00, 63)
Unknown	22	1
hr	82 (70.00, 98)	85 (73.00, 101)
Unknown	24	4
shock
0	297 (58%)	246 (56%)
1	215 (42%)	194 (44%)
Unknown	24	4
bil	1 (0.90, 3)	2 (1.00, 3)
Unknown	28	9
ast	56 (33.00, 96)	49 (32.00, 89)
Unknown	18	7
alt	29 (20.00, 47)	29 (19.00, 44)
Unknown	18	7
wbc	7,330 (5,315.00, 10,395)	8,300 (5,800.00, 11,100)
Unknown	13	7
hb	8 (6.90, 10)	9 (7.20, 10)
Unknown	13	7
plt	97 (72.00, 135)	103 (75.00, 141)
Unknown	13	7
tp	6 (5.50, 7)	6 (5.50, 7)
Unknown	45	32
alb	3 (2.50, 3)	3 (2.50, 3)
Unknown	34	12
eGFR	67 (48.25, 89)	68 (49.43, 89)
Unknown	14	7
bun	23 (15.85, 36)	25 (16.60, 36)
Unknown	14	7
cre	1 (0.67, 1)	1 (0.65, 1)
Unknown	18	10
crp	0 (0.11, 1)	0 (0.12, 1)
Unknown	37	15
pt	51 (40.00, 63)	59 (46.00, 72)
Unknown	39	20
aptt	29 (25.90, 33)	32 (29.65, 37)
Unknown	63	49
meld	11 (7.00, 15)	10 (6.00, 15)
Unknown	54	26
hosp_mortality	73 (14%)	45 (10%)
los	10 (6.00, 18)	7 (5.00, 13)
1 Median (IQR); n (%)

全体での連続量の可視化

df |> 　#全体
  select(col_continuous) |> 
  pivot_longer(cols = col_continuous, names_to = "name", values_to = "value") |> 
  ggplot()+
  geom_histogram(aes(x = value), color = "black")+
  facet_wrap(~ name, scales = "free", ncol = 5) +
  theme_bw()+
  theme(text = element_text(size = 12))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 1124 rows containing non-finite values (`stat_bin()`).

開発コホートtableone作成

col_cont = c("age", "bmi","smoke","child_num","gcs","cci_num","map","bt","sBP","dBP","hr","bil","ast","alt","wbc","hb","plt","tp","alb","eGFR","bun","cre","crp","pt","aptt","los","meld")
col_fact = c("sex","barthel","child_score","pad","stroke","dimentia","ch_lung","rheumati","pept_ulcer","dm","dm_compli","paralysis","malignancy","meta_tumor","aids","eGFR30","hd","hcc","alcohol","past_rupture","antiplate","anticoag","antithro","nsaids","steroid","beta", "vaso","ffp","pc", "albner","shock","hosp_mortality")

# Create your table
df_dev %>%  
  select(c(col_cont, col_fact)) %>% 
  CreateTableOne(vars = c(col_cont, col_fact), strata="hosp_mortality",factorVars = col_fact, addOverall = T) -> tableone_dev

## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
##   # Was:
##   data %>% select(col_cont)
## 
##   # Now:
##   data %>% select(all_of(col_cont))
## 
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
##   # Was:
##   data %>% select(col_fact)
## 
##   # Now:
##   data %>% select(all_of(col_fact))
## 
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

# Print your table
print(tableone_dev, smd = TRUE, missing = TRUE, test = TRUE, explain = TRUE) 

##                         Stratified by hosp_mortality
##                          Overall           0                 1                
##   n                          536               463                73          
##   age (mean (SD))          61.23 (13.06)     60.82 (13.02)     63.84 (13.09)  
##   bmi (mean (SD))          22.91 (3.73)      23.02 (3.76)      22.17 (3.41)   
##   smoke (mean (SD))       299.62 (571.82)   283.65 (394.78)   406.02 (1214.88)
##   child_num (mean (SD))     8.77 (2.13)       8.47 (1.95)      10.91 (2.13)   
##   gcs (mean (SD))          14.33 (2.20)      14.72 (1.27)      11.90 (4.33)   
##   cci_num (mean (SD))       4.44 (1.20)       4.43 (1.08)       4.53 (1.78)   
##   map (mean (SD))           3.49 (4.51)       3.10 (4.35)       5.96 (4.71)   
##   bt (mean (SD))           36.65 (0.76)      36.68 (0.67)      36.48 (1.24)   
##   sBP (mean (SD))          87.35 (15.48)     90.12 (13.62)     69.74 (15.05)  
##   dBP (mean (SD))          51.00 (11.90)     52.83 (11.00)     39.39 (10.83)  
##   hr (mean (SD))           85.58 (21.10)     83.17 (19.18)    100.84 (25.93)  
##   bil (mean (SD))           2.20 (2.08)       1.97 (1.80)       3.69 (2.92)   
##   ast (mean (SD))          85.41 (92.54)     79.65 (88.38)    121.08 (109.08) 
##   alt (mean (SD))          43.36 (50.96)     41.65 (50.23)     53.94 (54.43)  
##   wbc (mean (SD))        8348.30 (4401.79) 8193.93 (4211.45) 9299.86 (5366.73)
##   hb (mean (SD))            8.55 (2.46)       8.62 (2.47)       8.11 (2.38)   
##   plt (mean (SD))         110.70 (57.80)    109.10 (52.04)    120.60 (84.92)  
##   tp (mean (SD))            6.09 (0.93)       6.15 (0.86)       5.75 (1.21)   
##   alb (mean (SD))           2.82 (0.59)       2.90 (0.54)       2.30 (0.62)   
##   eGFR (mean (SD))         69.15 (30.82)     72.85 (30.55)     46.36 (21.32)  
##   bun (mean (SD))          28.41 (18.64)     27.53 (17.80)     33.94 (22.59)  
##   cre (mean (SD))           1.07 (0.92)       1.00 (0.92)       1.46 (0.82)   
##   crp (mean (SD))           0.81 (1.67)       0.71 (1.59)       1.46 (1.96)   
##   pt (mean (SD))           51.73 (16.99)     53.40 (16.04)     41.66 (19.06)  
##   aptt (mean (SD))         32.43 (17.30)     30.76 (12.33)     42.18 (32.59)  
##   los (mean (SD))          13.77 (15.28)     14.21 (14.24)     11.01 (20.59)  
##   meld (mean (SD))         12.13 (6.16)      11.04 (4.91)      18.84 (8.52)   
##   sex = F (%)                131 ( 24.4)       111 ( 24.0)        20 ( 27.4)  
##   barthel (%)                                                                 
##      0                       142 ( 35.9)       136 ( 39.8)         6 ( 11.1)  
##      1                       118 ( 29.8)       108 ( 31.6)        10 ( 18.5)  
##      2                       136 ( 34.3)        98 ( 28.7)        38 ( 70.4)  
##   child_score (%)                                                             
##      0                        66 ( 13.9)        65 ( 15.9)         1 (  1.5)  
##      1                       231 ( 48.7)       217 ( 53.1)        14 ( 21.5)  
##      2                       177 ( 37.3)       127 ( 31.1)        50 ( 76.9)  
##   pad = 1 (%)                  1 (  0.2)         1 (  0.2)         0 (  0.0)  
##   stroke = 1 (%)              10 (  1.9)        10 (  2.2)         0 (  0.0)  
##   dimentia = 1 (%)             4 (  0.7)         4 (  0.9)         0 (  0.0)  
##   ch_lung = 1 (%)             11 (  2.1)         9 (  1.9)         2 (  2.7)  
##   rheumati = 1 (%)             3 (  0.6)         3 (  0.6)         0 (  0.0)  
##   pept_ulcer = 1 (%)          59 ( 11.0)        58 ( 12.5)         1 (  1.4)  
##   dm = 1 (%)                 106 ( 19.8)        98 ( 21.2)         8 ( 11.0)  
##   dm_compli = 1 (%)            6 (  1.1)         6 (  1.3)         0 (  0.0)  
##   paralysis = 0 (%)          536 (100.0)       463 (100.0)        73 (100.0)  
##   malignancy = 1 (%)          58 ( 10.8)        46 (  9.9)        12 ( 16.4)  
##   meta_tumor = 1 (%)           6 (  1.1)         2 (  0.4)         4 (  5.5)  
##   aids = 0 (%)               536 (100.0)       463 (100.0)        73 (100.0)  
##   eGFR30 = 1 (%)              51 (  9.8)        34 (  7.6)        17 ( 23.3)  
##   hd = 1 (%)                   3 (  0.6)         3 (  0.6)         0 (  0.0)  
##   hcc = 1 (%)                111 ( 20.7)        95 ( 20.5)        16 ( 21.9)  
##   alcohol = 1 (%)            231 ( 43.1)       205 ( 44.3)        26 ( 35.6)  
##   past_rupture = 1 (%)       104 ( 19.4)        96 ( 20.7)         8 ( 11.0)  
##   antiplate = 1 (%)            4 (  0.7)         3 (  0.6)         1 (  1.4)  
##   anticoag = 1 (%)             1 (  0.2)         1 (  0.2)         0 (  0.0)  
##   antithro = 1 (%)             4 (  0.7)         3 (  0.6)         1 (  1.4)  
##   nsaids = 1 (%)               5 (  0.9)         5 (  1.1)         0 (  0.0)  
##   steroid = 1 (%)              2 (  0.4)         1 (  0.2)         1 (  1.4)  
##   beta = 1 (%)                18 (  3.4)        18 (  3.9)         0 (  0.0)  
##   vaso = 1 (%)                31 (  5.8)        11 (  2.4)        20 ( 27.4)  
##   ffp = 1 (%)                137 ( 25.6)       100 ( 21.6)        37 ( 50.7)  
##   pc = 1 (%)                   6 (  1.1)         3 (  0.6)         3 (  4.1)  
##   albner = 1 (%)              47 (  8.8)        32 (  6.9)        15 ( 20.5)  
##   shock = 1 (%)              215 ( 42.0)       155 ( 35.1)        60 ( 85.7)  
##   hosp_mortality = 1 (%)      73 ( 13.6)         0 (  0.0)        73 (100.0)  
##                         Stratified by hosp_mortality
##                          p      test SMD    Missing
##   n                                                
##   age (mean (SD))         0.066       0.231  0.0   
##   bmi (mean (SD))         0.108       0.236 15.9   
##   smoke (mean (SD))       0.125       0.135 15.7   
##   child_num (mean (SD))  <0.001       1.191 15.1   
##   gcs (mean (SD))        <0.001       0.882  0.0   
##   cci_num (mean (SD))     0.490       0.071  0.0   
##   map (mean (SD))        <0.001       0.630  0.0   
##   bt (mean (SD))          0.056       0.197  6.0   
##   sBP (mean (SD))        <0.001       1.420  4.1   
##   dBP (mean (SD))        <0.001       1.231  4.1   
##   hr (mean (SD))         <0.001       0.775  4.5   
##   bil (mean (SD))        <0.001       0.708  5.2   
##   ast (mean (SD))        <0.001       0.417  3.4   
##   alt (mean (SD))         0.057       0.235  3.4   
##   wbc (mean (SD))         0.046       0.229  2.4   
##   hb (mean (SD))          0.099       0.211  2.4   
##   plt (mean (SD))         0.115       0.163  2.4   
##   tp (mean (SD))          0.001       0.381  8.4   
##   alb (mean (SD))        <0.001       1.019  6.3   
##   eGFR (mean (SD))       <0.001       1.006  2.6   
##   bun (mean (SD))         0.007       0.316  2.6   
##   cre (mean (SD))        <0.001       0.533  3.4   
##   crp (mean (SD))        <0.001       0.421  6.9   
##   pt (mean (SD))         <0.001       0.667  7.3   
##   aptt (mean (SD))       <0.001       0.463 11.8   
##   los (mean (SD))         0.097       0.180  0.0   
##   meld (mean (SD))       <0.001       1.121 10.1   
##   sex = F (%)             0.627       0.078  0.0   
##   barthel (%)            <0.001       0.955 26.1   
##      0                                             
##      1                                             
##      2                                             
##   child_score (%)        <0.001       1.072 11.6   
##      0                                             
##      1                                             
##      2                                             
##   pad = 1 (%)             1.000       0.066  0.0   
##   stroke = 1 (%)          0.422       0.210  0.0   
##   dimentia = 1 (%)        0.948       0.132  0.0   
##   ch_lung = 1 (%)         0.999       0.053  0.0   
##   rheumati = 1 (%)        1.000       0.114  0.0   
##   pept_ulcer = 1 (%)      0.009       0.450  0.0   
##   dm = 1 (%)              0.061       0.281  0.0   
##   dm_compli = 1 (%)       0.704       0.162  0.0   
##   paralysis = 0 (%)          NA      <0.001  0.0   
##   malignancy = 1 (%)      0.144       0.193  0.0   
##   meta_tumor = 1 (%)      0.001       0.301  0.0   
##   aids = 0 (%)               NA      <0.001  0.0   
##   eGFR30 = 1 (%)         <0.001       0.446  2.6   
##   hd = 1 (%)              1.000       0.114  0.0   
##   hcc = 1 (%)             0.905       0.034  0.0   
##   alcohol = 1 (%)         0.207       0.178  0.0   
##   past_rupture = 1 (%)    0.071       0.270  0.0   
##   antiplate = 1 (%)       1.000       0.072  0.0   
##   anticoag = 1 (%)        1.000       0.066  0.0   
##   antithro = 1 (%)        1.000       0.072  0.0   
##   nsaids = 1 (%)          0.813       0.148  0.0   
##   steroid = 1 (%)         0.638       0.130  0.0   
##   beta = 1 (%)            0.173       0.284  0.0   
##   vaso = 1 (%)           <0.001       0.751  0.0   
##   ffp = 1 (%)            <0.001       0.635  0.0   
##   pc = 1 (%)              0.044       0.229  0.0   
##   albner = 1 (%)         <0.001       0.404  0.0   
##   shock = 1 (%)          <0.001       1.210  4.5   
##   hosp_mortality = 1 (%) <0.001         NaN  0.0

開発データでもtbl_summaryでmedian+IQRも準備する

# specify your data and variables
tbl_summary(data = df_dev, 
            by = "hosp_mortality",
            type = list(gcs ~ "continuous", year ~ "categorical"),
            statistic = all_continuous() ~ "{median} ({p25}, {p75})",
            digits = all_continuous() ~ c(0, 2))

Characteristic	0, N = 4631	1, N = 731
hosp_id	1,010 (1,003.00, 1,024)	1,017 (1,005.00, 1,024)
pt_id	359 (149.50, 583)	426 (235.00, 599)
hosp_num
1	404 (87%)	68 (93%)
2	43 (9.3%)	5 (6.8%)
3	15 (3.2%)	0 (0%)
4	1 (0.2%)	0 (0%)
year
2010	68 (15%)	8 (11%)
2011	70 (15%)	9 (12%)
2012	72 (16%)	16 (22%)
2013	55 (12%)	15 (21%)
2014	69 (15%)	8 (11%)
2015	67 (14%)	5 (6.8%)
2016	62 (13%)	12 (16%)
age	62 (51.00, 70)	66 (55.00, 74)
sex
M	352 (76%)	53 (73%)
F	111 (24%)	20 (27%)
bmi	23 (20.39, 25)	22 (20.15, 24)
Unknown	69	16
smoke	60 (0.00, 440)	0 (0.00, 400)
Unknown	70	14
barthel
0	136 (40%)	6 (11%)
1	108 (32%)	10 (19%)
2	98 (29%)	38 (70%)
Unknown	121	19
child_num	8 (7.00, 10)	11 (9.00, 12)
Unknown	64	17
child_score
0	65 (16%)	1 (1.5%)
1	217 (53%)	14 (22%)
2	127 (31%)	50 (77%)
Unknown	54	8
gcs	15 (15.00, 15)	15 (9.00, 15)
cci_num	4 (4.00, 5)	4 (4.00, 5)
pad
0	462 (100%)	73 (100%)
1	1 (0.2%)	0 (0%)
stroke
0	453 (98%)	73 (100%)
1	10 (2.2%)	0 (0%)
dimentia
0	459 (99%)	73 (100%)
1	4 (0.9%)	0 (0%)
ch_lung
0	454 (98%)	71 (97%)
1	9 (1.9%)	2 (2.7%)
rheumati
0	460 (99%)	73 (100%)
1	3 (0.6%)	0 (0%)
pept_ulcer
0	405 (87%)	72 (99%)
1	58 (13%)	1 (1.4%)
dm
0	365 (79%)	65 (89%)
1	98 (21%)	8 (11%)
dm_compli
0	457 (99%)	73 (100%)
1	6 (1.3%)	0 (0%)
paralysis
0	463 (100%)	73 (100%)
malignancy
0	417 (90%)	61 (84%)
1	46 (9.9%)	12 (16%)
meta_tumor
0	461 (100%)	69 (95%)
1	2 (0.4%)	4 (5.5%)
aids
0	463 (100%)	73 (100%)
eGFR30
0	415 (92%)	56 (77%)
1	34 (7.6%)	17 (23%)
Unknown	14	0
hd
0	460 (99%)	73 (100%)
1	3 (0.6%)	0 (0%)
hcc
0	368 (79%)	57 (78%)
1	95 (21%)	16 (22%)
alcohol
0	258 (56%)	47 (64%)
1	205 (44%)	26 (36%)
past_rupture
0	367 (79%)	65 (89%)
1	96 (21%)	8 (11%)
antiplate
0	460 (99%)	72 (99%)
1	3 (0.6%)	1 (1.4%)
anticoag
0	462 (100%)	73 (100%)
1	1 (0.2%)	0 (0%)
antithro
0	460 (99%)	72 (99%)
1	3 (0.6%)	1 (1.4%)
nsaids
0	458 (99%)	73 (100%)
1	5 (1.1%)	0 (0%)
steroid
0	462 (100%)	72 (99%)
1	1 (0.2%)	1 (1.4%)
beta
0	445 (96%)	73 (100%)
1	18 (3.9%)	0 (0%)
vaso
0	452 (98%)	53 (73%)
1	11 (2.4%)	20 (27%)
map	4 (0.00, 4)	6 (4.00, 8)
ffp
0	363 (78%)	36 (49%)
1	100 (22%)	37 (51%)
pc
0	460 (99%)	70 (96%)
1	3 (0.6%)	3 (4.1%)
albner
0	431 (93%)	58 (79%)
1	32 (6.9%)	15 (21%)
bt	37 (36.30, 37)	36 (36.00, 37)
Unknown	22	10
sBP	90 (82.00, 98)	64 (59.00, 79)
Unknown	19	3
dBP	52 (46.00, 60)	39 (33.00, 46)
Unknown	19	3
hr	80 (70.00, 94)	101 (89.00, 112)
Unknown	21	3
shock
0	287 (65%)	10 (14%)
1	155 (35%)	60 (86%)
Unknown	21	3
bil	1 (0.90, 2)	3 (1.53, 5)
Unknown	24	4
ast	53 (32.00, 92)	82 (48.75, 153)
Unknown	17	1
alt	28 (20.00, 44)	33 (25.00, 63)
Unknown	17	1
wbc	7,215 (5,300.00, 10,300)	7,990 (6,200.00, 10,800)
Unknown	13	0
hb	8 (7.00, 10)	8 (6.60, 10)
Unknown	13	0
plt	97 (72.00, 135)	98 (76.00, 130)
Unknown	13	0
tp	6 (5.50, 7)	6 (5.10, 6)
Unknown	37	8
alb	3 (2.58, 3)	2 (1.80, 3)
Unknown	31	3
eGFR	71 (52.10, 92)	44 (30.40, 58)
Unknown	14	0
bun	23 (15.25, 35)	28 (17.98, 45)
Unknown	13	1
cre	1 (0.65, 1)	1 (0.99, 2)
Unknown	18	0
crp	0 (0.10, 1)	1 (0.37, 2)
Unknown	33	4
pt	53 (42.00, 64)	40 (26.90, 56)
Unknown	37	2
aptt	28 (25.40, 33)	32 (29.10, 44)
Unknown	59	4
meld	10 (7.00, 14)	16 (13.00, 24)
Unknown	48	6
los	10 (7.00, 18)	4 (1.00, 16)
cohort
develop	463 (100%)	73 (100%)
validation	0 (0%)	0 (0%)
1 Median (IQR); n (%)

開発コホート 連続量可視化

df_dev |> 　#全体
  select(col_continuous) |> 
  pivot_longer(cols = col_continuous, names_to = "name", values_to = "value") |> 
  ggplot()+
  geom_histogram(aes(x = value), color = "black")+
  facet_wrap(~ name, scales = "free", ncol = 5) +
  theme_bw()+
  theme(text = element_text(size = 12))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 771 rows containing non-finite values (`stat_bin()`).

検証コホートtableone作成

# Create your table
df_val %>%  
  select(c(col_cont, col_fact)) %>% 
  CreateTableOne(vars = c(col_cont, col_fact), strata="hosp_mortality",factorVars = col_fact, addOverall = T) -> tableone_val


# Print your table
print(tableone_val, smd = TRUE, missing = TRUE, test = TRUE, explain = TRUE) 

##                         Stratified by hosp_mortality
##                          Overall           0                 1                 
##   n                          444               399                 45          
##   age (mean (SD))          60.42 (12.96)     60.18 (13.03)      62.62 (12.19)  
##   bmi (mean (SD))          24.38 (16.12)     24.46 (16.74)      23.43 (4.22)   
##   smoke (mean (SD))       226.73 (356.99)   233.57 (364.62)    157.22 (261.19) 
##   child_num (mean (SD))     8.38 (2.09)       8.13 (1.93)       10.52 (2.14)   
##   gcs (mean (SD))          14.61 (1.34)      14.66 (1.24)       14.09 (1.94)   
##   cci_num (mean (SD))       4.50 (1.45)       4.46 (1.36)        4.91 (2.11)   
##   map (mean (SD))           3.21 (3.18)       2.88 (2.76)        6.13 (4.81)   
##   bt (mean (SD))           36.81 (0.65)      36.81 (0.54)       36.84 (1.30)   
##   sBP (mean (SD))          89.95 (17.16)     92.40 (15.70)      68.33 (14.21)  
##   dBP (mean (SD))          54.84 (13.33)     56.33 (12.84)      41.69 (10.03)  
##   hr (mean (SD))           88.43 (20.93)     86.76 (20.28)     103.84 (20.79)  
##   bil (mean (SD))           2.42 (2.32)       2.18 (1.86)        4.54 (4.21)   
##   ast (mean (SD))          82.05 (109.78)    72.39 (79.19)     168.27 (237.83) 
##   alt (mean (SD))          39.93 (41.41)     36.47 (27.91)      70.91 (95.88)  
##   wbc (mean (SD))        9068.42 (5108.48) 8834.15 (5148.20) 11160.91 (4243.62)
##   hb (mean (SD))            8.92 (2.51)       9.00 (2.55)        8.18 (1.96)   
##   plt (mean (SD))         117.76 (75.39)    118.45 (77.27)     111.57 (56.24)  
##   tp (mean (SD))            6.13 (0.92)       6.19 (0.90)        5.64 (1.03)   
##   alb (mean (SD))           2.90 (0.64)       2.97 (0.61)        2.29 (0.54)   
##   eGFR (mean (SD))         70.59 (31.31)     73.60 (30.92)      43.71 (20.02)  
##   bun (mean (SD))          28.64 (17.04)     27.83 (16.43)      35.88 (20.60)  
##   cre (mean (SD))           1.05 (0.89)       1.00 (0.87)        1.54 (0.88)   
##   crp (mean (SD))           0.87 (1.86)       0.74 (1.64)        2.03 (3.01)   
##   pt (mean (SD))           58.78 (17.72)     60.61 (16.94)      43.01 (16.65)  
##   aptt (mean (SD))         36.06 (17.52)     34.27 (13.26)      51.43 (34.63)  
##   los (mean (SD))          10.67 (13.77)     10.72 (13.83)      10.18 (13.38)  
##   meld (mean (SD))         11.66 (5.97)      10.71 (4.98)       19.75 (7.47)   
##   sex = F (%)                115 ( 25.9)       103 ( 25.8)         12 ( 26.7)  
##   barthel (%)                                                                  
##      0                       171 ( 40.7)       167 ( 44.3)          4 (  9.3)  
##      1                       131 ( 31.2)       120 ( 31.8)         11 ( 25.6)  
##      2                       118 ( 28.1)        90 ( 23.9)         28 ( 65.1)  
##   child_score (%)                                                              
##      0                        83 ( 20.3)        81 ( 22.3)          2 (  4.4)  
##      1                       206 ( 50.4)       193 ( 53.0)         13 ( 28.9)  
##      2                       120 ( 29.3)        90 ( 24.7)         30 ( 66.7)  
##   pad = 1 (%)                  1 (  0.2)         1 (  0.3)          0 (  0.0)  
##   stroke = 1 (%)              11 (  2.5)         8 (  2.0)          3 (  6.7)  
##   dimentia = 1 (%)             6 (  1.4)         5 (  1.3)          1 (  2.2)  
##   ch_lung = 1 (%)              4 (  0.9)         3 (  0.8)          1 (  2.2)  
##   rheumati = 0 (%)           444 (100.0)       399 (100.0)         45 (100.0)  
##   pept_ulcer = 1 (%)          42 (  9.5)        40 ( 10.0)          2 (  4.4)  
##   dm = 1 (%)                 104 ( 23.4)        96 ( 24.1)          8 ( 17.8)  
##   dm_compli = 1 (%)            7 (  1.6)         6 (  1.5)          1 (  2.2)  
##   paralysis = 0 (%)          444 (100.0)       399 (100.0)         45 (100.0)  
##   malignancy = 1 (%)          57 ( 12.8)        50 ( 12.5)          7 ( 15.6)  
##   meta_tumor = 1 (%)          11 (  2.5)         7 (  1.8)          4 (  8.9)  
##   aids = 0 (%)               444 (100.0)       399 (100.0)         45 (100.0)  
##   eGFR30 = 1 (%)              36 (  8.2)        22 (  5.6)         14 ( 31.8)  
##   hd = 1 (%)                  10 (  2.3)         7 (  1.8)          3 (  6.7)  
##   hcc = 1 (%)                 64 ( 14.4)        56 ( 14.0)          8 ( 17.8)  
##   alcohol = 1 (%)            241 ( 54.3)       212 ( 53.1)         29 ( 64.4)  
##   past_rupture = 1 (%)       121 ( 27.3)       115 ( 28.8)          6 ( 13.3)  
##   antiplate = 1 (%)            3 (  0.7)         2 (  0.5)          1 (  2.2)  
##   anticoag = 1 (%)             3 (  0.7)         3 (  0.8)          0 (  0.0)  
##   antithro = 1 (%)             6 (  1.4)         5 (  1.3)          1 (  2.2)  
##   nsaids = 1 (%)               4 (  0.9)         4 (  1.0)          0 (  0.0)  
##   steroid = 1 (%)              3 (  0.7)         3 (  0.8)          0 (  0.0)  
##   beta = 1 (%)                40 (  9.0)        40 ( 10.0)          0 (  0.0)  
##   vaso = 1 (%)                27 (  6.1)        18 (  4.5)          9 ( 20.0)  
##   ffp = 1 (%)                154 ( 34.7)       126 ( 31.6)         28 ( 62.2)  
##   pc = 1 (%)                  11 (  2.5)         6 (  1.5)          5 ( 11.1)  
##   albner = 1 (%)              33 (  7.4)        26 (  6.5)          7 ( 15.6)  
##   shock = 1 (%)              194 ( 44.1)       153 ( 38.5)         41 ( 95.3)  
##   hosp_mortality = 1 (%)      45 ( 10.1)         0 (  0.0)         45 (100.0)  
##                         Stratified by hosp_mortality
##                          p      test SMD    Missing
##   n                                                
##   age (mean (SD))         0.230       0.194  0.0   
##   bmi (mean (SD))         0.730       0.084  6.8   
##   smoke (mean (SD))       0.221       0.241  9.5   
##   child_num (mean (SD))  <0.001       1.174 10.8   
##   gcs (mean (SD))         0.006       0.353  0.0   
##   cci_num (mean (SD))     0.048       0.255  0.0   
##   map (mean (SD))        <0.001       0.828  0.0   
##   bt (mean (SD))          0.785       0.029  1.1   
##   sBP (mean (SD))        <0.001       1.607  0.2   
##   dBP (mean (SD))        <0.001       1.271  0.2   
##   hr (mean (SD))         <0.001       0.832  0.9   
##   bil (mean (SD))        <0.001       0.724  2.0   
##   ast (mean (SD))        <0.001       0.541  1.6   
##   alt (mean (SD))        <0.001       0.488  1.6   
##   wbc (mean (SD))         0.004       0.493  1.6   
##   hb (mean (SD))          0.040       0.361  1.6   
##   plt (mean (SD))         0.566       0.102  1.6   
##   tp (mean (SD))         <0.001       0.566  7.2   
##   alb (mean (SD))        <0.001       1.177  2.7   
##   eGFR (mean (SD))       <0.001       1.148  1.6   
##   bun (mean (SD))         0.003       0.432  1.6   
##   cre (mean (SD))        <0.001       0.623  2.3   
##   crp (mean (SD))        <0.001       0.532  3.4   
##   pt (mean (SD))         <0.001       1.048  4.5   
##   aptt (mean (SD))       <0.001       0.654 11.0   
##   los (mean (SD))         0.801       0.040  0.0   
##   meld (mean (SD))       <0.001       1.424  5.9   
##   sex = F (%)             1.000       0.019  0.0   
##   barthel (%)            <0.001       1.041  5.4   
##      0                                             
##      1                                             
##      2                                             
##   child_score (%)        <0.001       0.972  7.9   
##      0                                             
##      1                                             
##      2                                             
##   pad = 1 (%)             1.000       0.071  0.0   
##   stroke = 1 (%)          0.161       0.230  0.0   
##   dimentia = 1 (%)        1.000       0.074  0.0   
##   ch_lung = 1 (%)         0.875       0.122  0.0   
##   rheumati = 0 (%)           NA      <0.001  0.0   
##   pept_ulcer = 1 (%)      0.345       0.217  0.0   
##   dm = 1 (%)              0.449       0.155  0.0   
##   dm_compli = 1 (%)       1.000       0.053  0.0   
##   paralysis = 0 (%)          NA      <0.001  0.0   
##   malignancy = 1 (%)      0.734       0.087  0.0   
##   meta_tumor = 1 (%)      0.016       0.322  0.0   
##   aids = 0 (%)               NA      <0.001  0.0   
##   eGFR30 = 1 (%)         <0.001       0.714  1.6   
##   hd = 1 (%)              0.115       0.246  0.0   
##   hcc = 1 (%)             0.650       0.102  0.0   
##   alcohol = 1 (%)         0.198       0.231  0.0   
##   past_rupture = 1 (%)    0.042       0.387  0.0   
##   antiplate = 1 (%)       0.707       0.149  0.0   
##   anticoag = 1 (%)        1.000       0.123  0.0   
##   antithro = 1 (%)        1.000       0.074  0.0   
##   nsaids = 1 (%)          1.000       0.142  0.0   
##   steroid = 1 (%)         1.000       0.123  0.0   
##   beta = 1 (%)            0.051       0.472  0.0   
##   vaso = 1 (%)           <0.001       0.486  0.0   
##   ffp = 1 (%)            <0.001       0.645  0.0   
##   pc = 1 (%)              0.001       0.403  0.0   
##   albner = 1 (%)          0.059       0.292  0.0   
##   shock = 1 (%)          <0.001       1.515  0.9   
##   hosp_mortality = 1 (%) <0.001         NaN  0.0

検証データでもtbl_summaryでmedian+IQRも準備する

# specify your data and variables
tbl_summary(data = df_val, 
            by = "hosp_mortality",
            type = list(gcs ~ "continuous", year ~ "categorical"),
            statistic = all_continuous() ~ "{median} ({p25}, {p75})",
            digits = all_continuous() ~ c(0, 2))

Characteristic	0, N = 3991	1, N = 451
hosp_id	1,022 (1,005.00, 1,024)	1,022 (1,006.00, 1,062)
pt_id	488 (232.50, 640)	510 (299.00, 738)
hosp_num
1	326 (82%)	39 (87%)
2	46 (12%)	4 (8.9%)
3	12 (3.0%)	1 (2.2%)
4	10 (2.5%)	0 (0%)
5	3 (0.8%)	1 (2.2%)
6	2 (0.5%)	0 (0%)
year
2017	57 (14%)	7 (16%)
2018	53 (13%)	9 (20%)
2019	63 (16%)	9 (20%)
2020	74 (19%)	8 (18%)
2021	76 (19%)	7 (16%)
2022	76 (19%)	5 (11%)
age	60 (50.00, 70)	64 (52.00, 70)
sex
M	296 (74%)	33 (73%)
F	103 (26%)	12 (27%)
bmi	23 (20.59, 26)	23 (20.16, 26)
Unknown	17	13
smoke	0 (0.00, 390)	0 (0.00, 312)
Unknown	33	9
barthel
0	167 (44%)	4 (9.3%)
1	120 (32%)	11 (26%)
2	90 (24%)	28 (65%)
Unknown	22	2
child_num	8 (7.00, 9)	11 (9.00, 12)
Unknown	45	3
child_score
0	81 (22%)	2 (4.4%)
1	193 (53%)	13 (29%)
2	90 (25%)	30 (67%)
Unknown	35	0
gcs	15 (15.00, 15)	15 (14.00, 15)
cci_num	4 (4.00, 5)	4 (4.00, 5)
pad
0	398 (100%)	45 (100%)
1	1 (0.3%)	0 (0%)
stroke
0	391 (98%)	42 (93%)
1	8 (2.0%)	3 (6.7%)
dimentia
0	394 (99%)	44 (98%)
1	5 (1.3%)	1 (2.2%)
ch_lung
0	396 (99%)	44 (98%)
1	3 (0.8%)	1 (2.2%)
rheumati
0	399 (100%)	45 (100%)
1	0 (0%)	0 (0%)
pept_ulcer
0	359 (90%)	43 (96%)
1	40 (10%)	2 (4.4%)
dm
0	303 (76%)	37 (82%)
1	96 (24%)	8 (18%)
dm_compli
0	393 (98%)	44 (98%)
1	6 (1.5%)	1 (2.2%)
paralysis
0	399 (100%)	45 (100%)
malignancy
0	349 (87%)	38 (84%)
1	50 (13%)	7 (16%)
meta_tumor
0	392 (98%)	41 (91%)
1	7 (1.8%)	4 (8.9%)
aids
0	399 (100%)	45 (100%)
eGFR30
0	371 (94%)	30 (68%)
1	22 (5.6%)	14 (32%)
Unknown	6	1
hd
0	392 (98%)	42 (93%)
1	7 (1.8%)	3 (6.7%)
hcc
0	343 (86%)	37 (82%)
1	56 (14%)	8 (18%)
alcohol
0	187 (47%)	16 (36%)
1	212 (53%)	29 (64%)
past_rupture
0	284 (71%)	39 (87%)
1	115 (29%)	6 (13%)
antiplate
0	397 (99%)	44 (98%)
1	2 (0.5%)	1 (2.2%)
anticoag
0	396 (99%)	45 (100%)
1	3 (0.8%)	0 (0%)
antithro
0	394 (99%)	44 (98%)
1	5 (1.3%)	1 (2.2%)
nsaids
0	395 (99%)	45 (100%)
1	4 (1.0%)	0 (0%)
steroid
0	396 (99%)	45 (100%)
1	3 (0.8%)	0 (0%)
beta
0	359 (90%)	45 (100%)
1	40 (10%)	0 (0%)
vaso
0	381 (95%)	36 (80%)
1	18 (4.5%)	9 (20%)
map	2 (0.00, 4)	4 (4.00, 8)
ffp
0	273 (68%)	17 (38%)
1	126 (32%)	28 (62%)
pc
0	393 (98%)	40 (89%)
1	6 (1.5%)	5 (11%)
albner
0	373 (93%)	38 (84%)
1	26 (6.5%)	7 (16%)
bt	37 (36.50, 37)	37 (36.20, 38)
Unknown	1	4
sBP	93 (82.25, 101)	68 (56.00, 79)
Unknown	1	0
dBP	56 (48.00, 64)	43 (34.00, 49)
Unknown	1	0
hr	83 (72.00, 100)	106 (90.50, 118)
Unknown	2	2
shock
0	244 (61%)	2 (4.7%)
1	153 (39%)	41 (95%)
Unknown	2	2
bil	2 (0.96, 3)	3 (1.63, 6)
Unknown	8	1
ast	47 (31.00, 84)	81 (39.00, 167)
Unknown	6	1
alt	29 (19.00, 42)	33 (18.75, 70)
Unknown	6	1
wbc	8,050 (5,600.00, 10,800)	9,850 (8,842.50, 13,925)
Unknown	6	1
hb	9 (7.30, 11)	8 (6.88, 9)
Unknown	6	1
plt	102 (75.00, 141)	109 (55.75, 147)
Unknown	6	1
tp	6 (5.60, 7)	6 (5.00, 6)
Unknown	30	2
alb	3 (2.50, 3)	2 (1.80, 3)
Unknown	11	1
eGFR	71 (53.87, 92)	41 (28.14, 58)
Unknown	6	1
bun	25 (16.40, 36)	32 (19.82, 48)
Unknown	6	1
cre	1 (0.64, 1)	1 (0.92, 2)
Unknown	9	1
crp	0 (0.11, 1)	1 (0.19, 2)
Unknown	13	2
pt	60 (48.22, 72)	41 (30.75, 54)
Unknown	19	1
aptt	32 (29.33, 36)	40 (35.30, 56)
Unknown	45	4
meld	10 (6.00, 14)	20 (14.00, 24)
Unknown	25	1
los	7 (5.00, 13)	6 (2.00, 12)
cohort
develop	0 (0%)	0 (0%)
validation	399 (100%)	45 (100%)
1 Median (IQR); n (%)

検証コホート 連続量可視化

df_val |> 　#全体
  select(col_continuous) |> 
  pivot_longer(cols = col_continuous, names_to = "name", values_to = "value") |> 
  ggplot()+
  geom_histogram(aes(x = value), color = "black")+
  facet_wrap(~ name, scales = "free", ncol = 5) +
  theme_bw()+
  theme(text = element_text(size = 12))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## Warning: Removed 353 rows containing non-finite values (`stat_bin()`).

連続変数の相関確認

数値で

# 連続変数のリストを指定します。
col_continu <- c("age", "bmi","smoke","child_num","gcs","cci_num","bt","sBP","dBP","hr","bil","ast","alt","wbc","hb","plt","tp","alb","eGFR","bun","cre","crp","pt","aptt")
# 指定した変数だけで相関行列を作成します。
corresult <- df_dev %>% 
  dplyr::select(all_of(col_continu)) %>% 
  drop_na() %>% 
  cor(method = "pearson")

# 相関行列の数値を表示します。
print(corresult)

##                    age           bmi       smoke   child_num          gcs
## age        1.000000000 -1.296100e-01 -0.13183344 -0.07904746 -0.056003460
## bmi       -0.129610011  1.000000e+00  0.05466316 -0.03675640 -0.002670049
## smoke     -0.131833435  5.466316e-02  1.00000000  0.11712280  0.012041939
## child_num -0.079047459 -3.675640e-02  0.11712280  1.00000000 -0.446313124
## gcs       -0.056003460 -2.670049e-03  0.01204194 -0.44631312  1.000000000
## cci_num    0.173806387  5.024066e-02  0.01130821  0.01885518  0.032132947
## bt        -0.105549686 -4.913514e-05 -0.02113526  0.05596907  0.055599410
## sBP       -0.050237673  2.058716e-02 -0.07168332 -0.33446966  0.249658005
## dBP       -0.065716671  2.963271e-03 -0.01202908 -0.26953193  0.248596331
## hr        -0.129842553 -8.735062e-02  0.15546953  0.28402598 -0.049489433
## bil       -0.231662689 -5.051886e-02  0.15359723  0.64601643 -0.255979204
## ast       -0.161693213 -1.337139e-01 -0.02897554  0.18680748 -0.050480785
## alt        0.003882512 -1.409287e-02 -0.04844208  0.06228414  0.014555916
## wbc       -0.193288481  3.516464e-02  0.11847396  0.22927340 -0.091775211
## hb        -0.058416009  1.827695e-01 -0.01758879 -0.22162265  0.065107613
## plt        0.092221345 -4.431667e-02 -0.08794923 -0.15424065  0.028328140
## tp        -0.085020719  3.998964e-02 -0.10721569 -0.20602561  0.028327021
## alb       -0.142467295  7.661253e-02 -0.09456042 -0.65673209  0.346804566
## eGFR      -0.421581160 -1.510401e-04 -0.02066158 -0.18092871  0.247110945
## bun        0.236651282 -4.360478e-02 -0.07590300  0.06459151 -0.137820967
## cre        0.093021049 -7.317137e-03  0.05553996  0.15426154 -0.243630164
## crp       -0.005293644 -6.675397e-02  0.02185618  0.19786931 -0.027971543
## pt         0.177417560  2.038415e-02 -0.15734504 -0.66164510  0.225617727
## aptt       0.032688987 -7.245584e-02  0.05439131  0.30180218 -0.163820383
##                cci_num            bt           sBP          dBP          hr
## age        0.173806387 -1.055497e-01 -0.0502376731 -0.065716671 -0.12984255
## bmi        0.050240657 -4.913514e-05  0.0205871583  0.002963271 -0.08735062
## smoke      0.011308208 -2.113526e-02 -0.0716833228 -0.012029085  0.15546953
## child_num  0.018855185  5.596907e-02 -0.3344696572 -0.269531928  0.28402598
## gcs        0.032132947  5.559941e-02  0.2496580047  0.248596331 -0.04948943
## cci_num    1.000000000 -1.281198e-01 -0.0811716905 -0.178497367 -0.13172033
## bt        -0.128119797  1.000000e+00  0.2368694297  0.164333733  0.25591612
## sBP       -0.081171691  2.368694e-01  1.0000000000  0.605357525 -0.15071133
## dBP       -0.178497367  1.643337e-01  0.6053575253  1.000000000 -0.10642854
## hr        -0.131720328  2.559161e-01 -0.1507113296 -0.106428543  1.00000000
## bil       -0.133648822  5.981601e-02 -0.1677211615 -0.221208944  0.31476430
## ast       -0.068234899  3.059386e-02 -0.0576587901 -0.036318866  0.15641394
## alt        0.132973609 -5.003688e-02 -0.0318011591 -0.047556489  0.02960149
## wbc       -0.090971027  8.325622e-02 -0.1625636450 -0.126387695  0.28203400
## hb        -0.006796172 -5.898742e-04  0.0920759203  0.113575537 -0.10008674
## plt        0.108837598 -1.072875e-01 -0.0617947620 -0.008897726 -0.05141042
## tp         0.027755188 -5.917779e-02  0.1792214811  0.077045024 -0.09569402
## alb       -0.043708852  1.711312e-02  0.3427230030  0.277426076 -0.17757371
## eGFR      -0.035233837  9.245822e-02  0.2033327260  0.158352541 -0.04424801
## bun        0.078270164 -3.851523e-02 -0.0528615394 -0.048348133  0.06432638
## cre        0.035292036 -4.830078e-02 -0.0377773508 -0.035203607  0.12673924
## crp       -0.021155411 -6.147842e-02 -0.0005477627 -0.023554640  0.02831096
## pt         0.010651701 -3.238337e-02  0.2486208414  0.237449213 -0.27288392
## aptt      -0.073689313  5.748142e-02 -0.1422777096 -0.179225336  0.13148526
##                    bil         ast          alt         wbc            hb
## age       -0.231662689 -0.16169321  0.003882512 -0.19328848 -0.0584160095
## bmi       -0.050518861 -0.13371394 -0.014092866  0.03516464  0.1827695297
## smoke      0.153597229 -0.02897554 -0.048442083  0.11847396 -0.0175887861
## child_num  0.646016426  0.18680748  0.062284144  0.22927340 -0.2216226477
## gcs       -0.255979204 -0.05048079  0.014555916 -0.09177521  0.0651076127
## cci_num   -0.133648822 -0.06823490  0.132973609 -0.09097103 -0.0067961722
## bt         0.059816013  0.03059386 -0.050036882  0.08325622 -0.0005898742
## sBP       -0.167721161 -0.05765879 -0.031801159 -0.16256365  0.0920759203
## dBP       -0.221208944 -0.03631887 -0.047556489 -0.12638769  0.1135755375
## hr         0.314764295  0.15641394  0.029601488  0.28203400 -0.1000867410
## bil        1.000000000  0.24515899  0.080686623  0.24509282  0.0052262177
## ast        0.245158987  1.00000000  0.713569279  0.23456177 -0.0865216624
## alt        0.080686623  0.71356928  1.000000000  0.11793436  0.0096813469
## wbc        0.245092818  0.23456177  0.117934355  1.00000000 -0.1655430038
## hb         0.005226218 -0.08652166  0.009681347 -0.16554300  1.0000000000
## plt       -0.174243770  0.05342028  0.082966951  0.30106793 -0.1054903948
## tp         0.012518151  0.04101746  0.023922312 -0.09972653  0.4019832235
## alb       -0.255644701 -0.08217463  0.015350205 -0.12925971  0.3884854516
## eGFR      -0.054887252 -0.02619620 -0.037626487 -0.14849069  0.2648169513
## bun        0.030376381  0.03514411  0.147371774  0.16964975 -0.2699004874
## cre        0.155929485  0.06726628  0.040052392  0.11988522 -0.1810436238
## crp        0.302861515  0.24460499  0.206884173  0.17776026 -0.0491261686
## pt        -0.503926564 -0.19129890 -0.098671705 -0.34108680  0.3188720028
## aptt       0.224658658  0.04295108 -0.001659784  0.08477517 -0.1081389027
##                    plt           tp         alb          eGFR         bun
## age        0.092221345 -0.085020719 -0.14246730 -0.4215811597  0.23665128
## bmi       -0.044316671  0.039989635  0.07661253 -0.0001510401 -0.04360478
## smoke     -0.087949228 -0.107215689 -0.09456042 -0.0206615776 -0.07590300
## child_num -0.154240647 -0.206025609 -0.65673209 -0.1809287110  0.06459151
## gcs        0.028328140  0.028327021  0.34680457  0.2471109454 -0.13782097
## cci_num    0.108837598  0.027755188 -0.04370885 -0.0352338370  0.07827016
## bt        -0.107287516 -0.059177786  0.01711312  0.0924582208 -0.03851523
## sBP       -0.061794762  0.179221481  0.34272300  0.2033327260 -0.05286154
## dBP       -0.008897726  0.077045024  0.27742608  0.1583525414 -0.04834813
## hr        -0.051410421 -0.095694019 -0.17757371 -0.0442480079  0.06432638
## bil       -0.174243770  0.012518151 -0.25564470 -0.0548872524  0.03037638
## ast        0.053420281  0.041017463 -0.08217463 -0.0261962029  0.03514411
## alt        0.082966951  0.023922312  0.01535021 -0.0376264873  0.14737177
## wbc        0.301067926 -0.099726529 -0.12925971 -0.1484906941  0.16964975
## hb        -0.105490395  0.401983223  0.38848545  0.2648169513 -0.26990049
## plt        1.000000000  0.089248410  0.06965940 -0.1080944525  0.13865202
## tp         0.089248410  1.000000000  0.45416125  0.1829208844 -0.15122064
## alb        0.069659398  0.454161248  1.00000000  0.2868053223 -0.07656109
## eGFR      -0.108094453  0.182920884  0.28680532  1.0000000000 -0.48614545
## bun        0.138652016 -0.151220644 -0.07656109 -0.4861454533  1.00000000
## cre        0.012580290 -0.075551213 -0.12332435 -0.6435014645  0.59022745
## crp        0.147401481  0.001746056 -0.13218785 -0.0529216750  0.09367348
## pt         0.138877637  0.225869452  0.48651726  0.1217545292 -0.09432596
## aptt      -0.109063601 -0.319854060 -0.42762353 -0.1874785739  0.11294775
##                    cre           crp          pt         aptt
## age        0.093021049 -0.0052936444  0.17741756  0.032688987
## bmi       -0.007317137 -0.0667539682  0.02038415 -0.072455842
## smoke      0.055539958  0.0218561798 -0.15734504  0.054391313
## child_num  0.154261541  0.1978693093 -0.66164510  0.301802176
## gcs       -0.243630164 -0.0279715426  0.22561773 -0.163820383
## cci_num    0.035292036 -0.0211554113  0.01065170 -0.073689313
## bt        -0.048300776 -0.0614784176 -0.03238337  0.057481422
## sBP       -0.037777351 -0.0005477627  0.24862084 -0.142277710
## dBP       -0.035203607 -0.0235546395  0.23744921 -0.179225336
## hr         0.126739240  0.0283109574 -0.27288392  0.131485257
## bil        0.155929485  0.3028615150 -0.50392656  0.224658658
## ast        0.067266283  0.2446049888 -0.19129890  0.042951078
## alt        0.040052392  0.2068841729 -0.09867171 -0.001659784
## wbc        0.119885215  0.1777602553 -0.34108680  0.084775171
## hb        -0.181043624 -0.0491261686  0.31887200 -0.108138903
## plt        0.012580290  0.1474014808  0.13887764 -0.109063601
## tp        -0.075551213  0.0017460561  0.22586945 -0.319854060
## alb       -0.123324350 -0.1321878521  0.48651726 -0.427623526
## eGFR      -0.643501465 -0.0529216750  0.12175453 -0.187478574
## bun        0.590227449  0.0936734761 -0.09432596  0.112947751
## cre        1.000000000  0.0716154896 -0.10466814  0.168143227
## crp        0.071615490  1.0000000000 -0.12359882  0.070965103
## pt        -0.104668145 -0.1235988181  1.00000000 -0.441098246
## aptt       0.168143227  0.0709651031 -0.44109825  1.000000000

相関プロット(拡大推奨)

# 指定した連続変数だけで相関行列を作成します。
corresult <- df_dev %>% 
  dplyr::select(all_of(col_continu)) %>% 
  drop_na() %>% 
  cor(method = "pearson")

# 相関行列を基に相関プロットを作成します。
corrplot <- ggcorrplot(corr = corresult, hc.order = FALSE, method = "square", title = "cor plot",
                       colors = c("#4b61ba", "white", "red"), lab = TRUE)

# プロットを表示します。
corrplot

bilとchild_numlが0.65と相関係数は高め。ただ他は許容できそう。

変数の線形評価

# 連続変数の変数名をまとめる
con_var <- c("age", "bmi","smoke","child_num","gcs","cci_num","map","bt","sBP","dBP","hr","bil","ast","alt","wbc","hb","plt","tp","alb","eGFR","bun","cre","crp","pt","aptt","meld")

# datadistを計算
ddist <- datadist(df_dev)
options(datadist='ddist')

# プロット結果をまとめるリストを用意する
plot <- list()

for (x in con_var){
  # lrmに投入するformulaを文字列で、for文で順番に指定していく
  formula_tmp <- as.formula(paste("hosp_mortality ~ rcs(", x, ", 4)"))
  fit_tmp <- lrm(formula_tmp, data = df_dev)
  
  # Predict関数の呼び出しを文字列として作成し、それをパースして評価する
  plot_cmd <- paste("plot(Predict(fit_tmp, ", x, "))")
  plot_tmp <- eval(parse(text = plot_cmd))
  plot[[x]] <- plot_tmp
}

# 結果をggarrangeでまとめて表示する
logOR_plot <- ggarrange(plotlist = plot, ncol = 1, nrow = 1)

logOR_plot

## $`1`

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## 
## attr(,"class")
## [1] "list"      "ggarrange"

欠測を可視化

その1

naplot_1 <- gg_miss_var(df_dev, show_pct = TRUE)

naplot_1

## Warning: The `guide` argument in `scale_*()` cannot be `FALSE`. This was deprecated in
## ggplot2 3.3.4.
## ℹ Please use "none" instead.
## ℹ The deprecated feature was likely used in the naniar package.
##   Please report the issue at <https://github.com/njtierney/naniar/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

その2

naplot_2 <- vis_miss(df_dev)

## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## ℹ Please use `gather()` instead.
## ℹ The deprecated feature was likely used in the visdat package.
##   Please report the issue at <https://github.com/ropensci/visdat/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

naplot_2

vari_cat.f <-c("sex","barthel","child_score","pad","stroke","dimentia","ch_lung","rheumati","pept_ulcer","dm","dm_compli","paralysis","malignancy","meta_tumor","aids","eGFR30","hd","hcc","alcohol","past_rupture","antiplate","anticoag","antithro","nsaids","steroid","beta", "vaso","ffp","pc", "albner","shock","hosp_mortality")


vari_numeric <- c("age", "bmi","smoke","child_num","gcs","cci_num","map","bt","sBP","dBP","hr","bil","ast","alt","wbc","hb","plt","tp","alb","eGFR","bun","cre","crp","pt","aptt","los","meld")

# vari_cat.f (defined as above) in the data_original is changed to factor type and set into data_factor_for_imp
data_factor_for_imp <- as.data.frame(lapply(df_dev[vari_cat.f],as.factor))
#check all the variable is factor
str(data_factor_for_imp)

## 'data.frame':    536 obs. of  32 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 1 2 1 2 1 2 1 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": NA 3 1 2 NA NA NA 1 NA 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 1 NA 2 NA NA NA 2 3 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 2 1 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 1 2 2 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 1 2 2 2 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 2 ...

#summary numeric type
data_numeric_for_imp <- as.data.frame(lapply(df_dev[vari_numeric],as.numeric))
# check all the variable is numeric
str(data_numeric_for_imp)

## 'data.frame':    536 obs. of  27 variables:
##  $ age      : num  50 80 59 44 67 65 49 73 69 62 ...
##  $ bmi      : num  NA 25.3 NA 14.5 NA ...
##  $ smoke    : num  0 0 0 240 0 0 NA 1000 0 0 ...
##  $ child_num: num  11 6 NA 8 NA NA NA 9 15 11 ...
##  $ gcs      : num  15 15 15 15 15 15 15 15 6 15 ...
##  $ cci_num  : num  4 4 3 4 4 3 4 4 4 4 ...
##  $ map      : num  0 2 0 6 2 2 0 4 14 6 ...
##  $ bt       : num  36.4 36.8 35.9 36 36.6 38.4 37 37 35.5 36.6 ...
##  $ sBP      : num  78 88 100 69 66 84 132 90 52 58 ...
##  $ dBP      : num  48 49 56 44 40 46 69 54 37 37 ...
##  $ hr       : num  118 72 110 104 72 127 83 114 98 106 ...
##  $ bil      : num  2.2 1.2 3.1 3.4 1.2 2.4 1.2 2.2 8.7 2.1 ...
##  $ ast      : num  217 31 60 129 52 90 154 55 96 121 ...
##  $ alt      : num  63 22 40 46 36 19 109 20 87 63 ...
##  $ wbc      : num  7400 5000 7800 9100 3900 8000 7900 11100 12800 8300 ...
##  $ hb       : num  6.9 10.8 9.7 10.7 6.3 9.8 13.5 5.6 6 9.8 ...
##  $ plt      : num  115 77 74 162 63 93 132 124 168 84 ...
##  $ tp       : num  6.3 5.6 6.4 6.1 5.1 7.2 7.6 4.9 5.3 6.3 ...
##  $ alb      : num  2.2 3.2 2.8 2.9 2.8 3.2 4 2.3 1.2 2.5 ...
##  $ eGFR     : num  58 57.7 63.7 112.4 123.6 ...
##  $ bun      : num  13.2 41.5 26.3 2.9 27.4 15.8 15.5 27 63.2 13.8 ...
##  $ cre      : num  1.08 0.96 0.72 0.61 0.38 0.44 0.4 0.97 2.07 0.87 ...
##  $ crp      : num  0.92 0.29 0.68 0.29 0.29 0.96 0.29 NA 2.75 0.12 ...
##  $ pt       : num  37.8 55 46.7 37.8 74.6 45.9 49.7 45.9 37.2 54 ...
##  $ aptt     : num  29.4 29 27.2 35.3 30.1 27.6 32.6 27.5 36 29.1 ...
##  $ los      : num  12 7 0 10 3 2 1 8 0 16 ...
##  $ meld     : num  16 11 13 14 6 8 6 15 29 12 ...

# combine the factor type and numeric type
data_for_imp <- cbind(data_factor_for_imp, data_numeric_for_imp) 
#check all the variable type
str(data_for_imp)

## 'data.frame':    536 obs. of  59 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 1 2 1 2 1 2 1 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": NA 3 1 2 NA NA NA 1 NA 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 1 NA 2 NA NA NA 2 3 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 2 1 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 1 2 2 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 1 2 2 2 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 2 ...
##  $ age           : num  50 80 59 44 67 65 49 73 69 62 ...
##  $ bmi           : num  NA 25.3 NA 14.5 NA ...
##  $ smoke         : num  0 0 0 240 0 0 NA 1000 0 0 ...
##  $ child_num     : num  11 6 NA 8 NA NA NA 9 15 11 ...
##  $ gcs           : num  15 15 15 15 15 15 15 15 6 15 ...
##  $ cci_num       : num  4 4 3 4 4 3 4 4 4 4 ...
##  $ map           : num  0 2 0 6 2 2 0 4 14 6 ...
##  $ bt            : num  36.4 36.8 35.9 36 36.6 38.4 37 37 35.5 36.6 ...
##  $ sBP           : num  78 88 100 69 66 84 132 90 52 58 ...
##  $ dBP           : num  48 49 56 44 40 46 69 54 37 37 ...
##  $ hr            : num  118 72 110 104 72 127 83 114 98 106 ...
##  $ bil           : num  2.2 1.2 3.1 3.4 1.2 2.4 1.2 2.2 8.7 2.1 ...
##  $ ast           : num  217 31 60 129 52 90 154 55 96 121 ...
##  $ alt           : num  63 22 40 46 36 19 109 20 87 63 ...
##  $ wbc           : num  7400 5000 7800 9100 3900 8000 7900 11100 12800 8300 ...
##  $ hb            : num  6.9 10.8 9.7 10.7 6.3 9.8 13.5 5.6 6 9.8 ...
##  $ plt           : num  115 77 74 162 63 93 132 124 168 84 ...
##  $ tp            : num  6.3 5.6 6.4 6.1 5.1 7.2 7.6 4.9 5.3 6.3 ...
##  $ alb           : num  2.2 3.2 2.8 2.9 2.8 3.2 4 2.3 1.2 2.5 ...
##  $ eGFR          : num  58 57.7 63.7 112.4 123.6 ...
##  $ bun           : num  13.2 41.5 26.3 2.9 27.4 15.8 15.5 27 63.2 13.8 ...
##  $ cre           : num  1.08 0.96 0.72 0.61 0.38 0.44 0.4 0.97 2.07 0.87 ...
##  $ crp           : num  0.92 0.29 0.68 0.29 0.29 0.96 0.29 NA 2.75 0.12 ...
##  $ pt            : num  37.8 55 46.7 37.8 74.6 45.9 49.7 45.9 37.2 54 ...
##  $ aptt          : num  29.4 29 27.2 35.3 30.1 27.6 32.6 27.5 36 29.1 ...
##  $ los           : num  12 7 0 10 3 2 1 8 0 16 ...
##  $ meld          : num  16 11 13 14 6 8 6 15 29 12 ...

missforestによる単一代入

cores <- detectCores(logical = FALSE) ###並列化処理
registerDoParallel(cores = cores) ###並列化処理
set.seed(2023)
md.pattern(data_for_imp) #see patern the missing

##     sex pad stroke dimentia ch_lung rheumati pept_ulcer dm dm_compli paralysis
## 216   1   1      1        1       1        1          1  1         1         1
## 72    1   1      1        1       1        1          1  1         1         1
## 16    1   1      1        1       1        1          1  1         1         1
## 5     1   1      1        1       1        1          1  1         1         1
## 25    1   1      1        1       1        1          1  1         1         1
## 10    1   1      1        1       1        1          1  1         1         1
## 9     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 9     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 9     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 14    1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 3     1   1      1        1       1        1          1  1         1         1
## 3     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 7     1   1      1        1       1        1          1  1         1         1
## 9     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 6     1   1      1        1       1        1          1  1         1         1
## 3     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 9     1   1      1        1       1        1          1  1         1         1
## 3     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 3     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 4     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 3     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 3     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 5     1   1      1        1       1        1          1  1         1         1
## 4     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
##       0   0      0        0       0        0          0  0         0         0
##     malignancy meta_tumor aids hd hcc alcohol past_rupture antiplate anticoag
## 216          1          1    1  1   1       1            1         1        1
## 72           1          1    1  1   1       1            1         1        1
## 16           1          1    1  1   1       1            1         1        1
## 5            1          1    1  1   1       1            1         1        1
## 25           1          1    1  1   1       1            1         1        1
## 10           1          1    1  1   1       1            1         1        1
## 9            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 9            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 9            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 14           1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 3            1          1    1  1   1       1            1         1        1
## 3            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 7            1          1    1  1   1       1            1         1        1
## 9            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 6            1          1    1  1   1       1            1         1        1
## 3            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 9            1          1    1  1   1       1            1         1        1
## 3            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 3            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 4            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 3            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 3            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 5            1          1    1  1   1       1            1         1        1
## 4            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
##              0          0    0  0   0       0            0         0        0
##     antithro nsaids steroid beta vaso ffp pc albner hosp_mortality age gcs
## 216        1      1       1    1    1   1  1      1              1   1   1
## 72         1      1       1    1    1   1  1      1              1   1   1
## 16         1      1       1    1    1   1  1      1              1   1   1
## 5          1      1       1    1    1   1  1      1              1   1   1
## 25         1      1       1    1    1   1  1      1              1   1   1
## 10         1      1       1    1    1   1  1      1              1   1   1
## 9          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 9          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 9          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 14         1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 3          1      1       1    1    1   1  1      1              1   1   1
## 3          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 7          1      1       1    1    1   1  1      1              1   1   1
## 9          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 6          1      1       1    1    1   1  1      1              1   1   1
## 3          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 9          1      1       1    1    1   1  1      1              1   1   1
## 3          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 3          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 4          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 3          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 3          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 5          1      1       1    1    1   1  1      1              1   1   1
## 4          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
##            0      0       0    0    0   0  0      0              0   0   0
##     cci_num map los wbc hb plt eGFR30 eGFR bun ast alt cre sBP dBP shock hr bil
## 216       1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 72        1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 16        1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 5         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 25        1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 10        1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 9         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 9         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 9         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 14        1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 3         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 3         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 7         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 9         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 6         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 3         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 9         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 3         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 3         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 4         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   0
## 3         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   0
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     1  1   0
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   1   1     0  0   1
## 3         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 2         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   1
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   0
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   0
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   0
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   0
## 1         1   1   1   1  1   1      1    1   1   1   1   1   0   0     0  0   0
## 1         1   1   1   1  1   1      1    1   1   1   1   0   1   1     1  1   1
## 2         1   1   1   1  1   1      1    1   1   1   1   0   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   1   1   0   1   1     1  1   1
## 1         1   1   1   1  1   1      1    1   1   0   0   1   1   1     1  1   0
## 1         1   1   1   1  1   1      1    1   1   0   0   1   1   1     1  1   0
## 1         1   1   1   1  1   1      1    1   1   0   0   1   1   1     1  1   0
## 1         1   1   1   1  1   1      1    1   1   0   0   1   1   1     1  1   0
## 1         1   1   1   1  1   1      1    1   0   0   0   1   1   1     1  1   0
## 1         1   1   1   1  1   1      0    0   1   1   1   0   1   1     1  1   1
## 1         1   1   1   1  1   1      0    0   0   0   0   0   1   1     1  1   0
## 1         1   1   1   0  0   0      1    1   1   1   1   1   1   1     1  1   1
## 5         1   1   1   0  0   0      0    0   0   0   0   0   1   1     1  1   0
## 4         1   1   1   0  0   0      0    0   0   0   0   0   1   1     1  1   0
## 2         1   1   1   0  0   0      0    0   0   0   0   0   0   0     0  0   0
## 1         1   1   1   0  0   0      0    0   0   0   0   0   0   0     0  0   0
##           0   0   0  13 13  13     14   14  14  18  18  18  22  22    24 24  28
##     bt alb crp pt tp meld child_score aptt child_num smoke bmi barthel     
## 216  1   1   1  1  1    1           1    1         1     1   1       1    0
## 72   1   1   1  1  1    1           1    1         1     1   1       0    1
## 16   1   1   1  1  1    1           1    1         1     1   0       1    1
## 5    1   1   1  1  1    1           1    1         1     1   0       0    2
## 25   1   1   1  1  1    1           1    1         1     0   1       1    1
## 10   1   1   1  1  1    1           1    1         1     0   1       0    2
## 9    1   1   1  1  1    1           1    1         1     0   0       1    2
## 1    1   1   1  1  1    1           1    1         1     0   0       0    3
## 9    1   1   1  1  1    1           1    1         0     1   1       1    1
## 2    1   1   1  1  1    1           1    1         0     1   1       0    2
## 1    1   1   1  1  1    1           1    1         0     1   0       1    2
## 1    1   1   1  1  1    1           1    1         0     1   0       0    3
## 2    1   1   1  1  1    1           1    1         0     0   0       1    3
## 1    1   1   1  1  1    1           1    1         0     0   0       0    4
## 9    1   1   1  1  1    1           1    0         1     1   1       1    1
## 1    1   1   1  1  1    1           1    0         1     1   1       0    2
## 2    1   1   1  1  1    1           1    0         1     0   1       1    2
## 1    1   1   1  1  1    1           1    0         1     0   1       0    3
## 2    1   1   1  1  1    1           1    0         1     0   0       1    3
## 14   1   1   1  1  1    1           0    1         0     1   1       1    2
## 2    1   1   1  1  1    1           0    1         0     1   1       0    3
## 3    1   1   1  1  1    1           0    1         0     1   0       1    3
## 3    1   1   1  1  1    1           0    1         0     1   0       0    4
## 1    1   1   1  1  1    1           0    1         0     0   1       1    3
## 7    1   1   1  1  1    1           0    1         0     0   0       1    4
## 9    1   1   1  1  1    1           0    1         0     0   0       0    5
## 1    1   1   1  1  1    1           0    0         0     1   1       0    4
## 1    1   1   1  1  1    1           0    0         0     0   0       1    5
## 6    1   1   1  1  0    1           1    1         1     1   1       1    1
## 3    1   1   1  1  0    1           1    1         1     1   1       0    2
## 2    1   1   1  1  0    1           1    1         1     1   0       1    2
## 2    1   1   1  1  0    1           1    0         1     1   1       1    2
## 9    1   1   1  0  1    0           1    0         1     1   1       1    3
## 3    1   1   1  0  1    0           1    0         1     1   1       0    4
## 2    1   1   1  0  1    0           0    0         0     1   1       1    5
## 2    1   1   0  1  1    1           1    1         1     1   1       1    1
## 2    1   1   0  1  1    1           1    1         1     1   1       0    2
## 1    1   1   0  1  1    1           1    1         1     1   0       1    2
## 1    1   1   0  1  1    1           1    1         1     0   1       1    2
## 1    1   1   0  1  1    1           1    1         1     0   0       1    3
## 1    1   1   0  1  1    1           1    1         1     0   0       0    4
## 1    1   1   0  1  1    1           1    0         1     0   0       1    4
## 1    1   1   0  1  1    1           0    1         0     1   0       0    5
## 1    1   1   0  1  1    1           0    0         0     0   0       0    7
## 1    1   1   0  1  0    1           1    1         1     1   1       1    2
## 2    1   1   0  0  1    0           1    0         1     1   1       1    4
## 1    1   0   1  1  1    1           1    1         1     1   1       0    2
## 1    1   0   1  1  1    1           1    1         1     0   1       0    3
## 3    1   0   1  1  0    1           1    1         1     1   1       1    2
## 1    1   0   1  0  1    0           1    0         1     1   1       0    5
## 1    1   0   1  0  1    0           0    0         0     1   1       1    6
## 1    1   0   1  0  0    0           1    0         1     1   1       1    5
## 1    1   0   0  0  0    0           1    0         1     1   1       1    6
## 4    0   1   1  1  1    1           1    1         1     1   1       0    2
## 1    0   1   1  1  1    1           1    1         1     0   1       1    2
## 1    0   1   1  1  1    1           1    1         1     0   1       0    3
## 1    0   1   1  1  1    1           1    1         0     1   1       1    2
## 1    0   1   1  1  1    1           1    1         0     0   0       0    5
## 1    0   1   0  1  1    1           0    1         0     1   1       0    5
## 1    0   0   1  1  1    1           1    1         1     0   0       1    4
## 1    1   1   1  1  1    0           1    1         1     1   0       1    3
## 3    1   1   1  1  1    0           0    1         0     1   1       1    4
## 1    1   0   0  1  0    0           1    1         1     0   0       1    7
## 1    1   1   1  1  1    1           1    1         1     1   1       0    3
## 1    1   1   1  1  1    1           0    1         0     1   1       1    4
## 3    0   1   1  1  1    1           1    1         1     1   1       0    6
## 1    0   1   1  1  1    1           0    1         0     0   0       1    9
## 1    0   1   1  1  0    1           1    1         1     1   0       1    7
## 1    0   1   0  1  1    1           1    1         1     1   1       1    6
## 1    0   1   0  1  0    1           1    1         1     1   1       1    7
## 1    0   0   1  1  0    1           1    1         1     1   1       1    7
## 1    0   0   1  1  0    1           1    1         1     1   1       0    8
## 2    0   0   1  1  0    1           1    1         1     1   0       1    8
## 1    0   0   1  1  0    1           1    1         1     1   0       0    9
## 1    0   0   1  1  0    1           0    1         0     1   0       1   10
## 1    0   0   1  0  0    0           1    0         1     1   1       1   10
## 1    0   1   1  1  1    0           1    1         0     1   1       1    8
## 1    0   1   1  1  1    0           1    0         1     1   1       1    8
## 1    0   1   1  1  1    0           0    1         0     1   1       1    9
## 1    0   1   1  0  1    0           1    0         1     1   0       1   10
## 1    0   1   0  0  1    0           1    0         1     1   1       1   10
## 1    1   1   1  1  1    0           1    1         1     1   1       1    2
## 2    1   1   1  1  1    0           1    1         1     1   0       1    3
## 1    1   1   1  1  1    0           0    1         0     1   1       1    4
## 1    1   1   1  0  1    0           1    0         1     1   1       0    7
## 1    1   1   0  0  1    0           1    0         1     1   0       0    9
## 1    1   0   0  1  0    0           0    0         0     0   0       0   13
## 1    1   0   0  0  0    0           1    0         1     1   1       1    9
## 1    1   0   0  1  0    0           0    0         0     1   1       1   11
## 1    1   1   0  1  1    0           1    1         1     1   0       1    6
## 1    1   0   1  0  0    0           1    0         1     1   1       0   13
## 1    1   1   1  1  1    1           1    1         1     1   1       1    3
## 5    1   0   0  0  0    0           1    0         1     1   1       1   16
## 4    1   0   0  0  0    0           0    0         0     1   1       1   18
## 2    0   0   0  0  0    0           1    0         1     1   1       1   21
## 1    0   0   0  0  0    0           0    0         0     1   1       1   23
##     32  34  37 39 45   54          62   63        81    84  85     140 1011

imp.mf <- missForest(data_for_imp,
                     maxiter = 10,
                     ntree = 100,
                     mtry = floor(sqrt(ncol(data_for_imp))),
                     parallelize = "variables",
                     verbose = TRUE)

##   parallelizing over the variables of the input data matrix 'xmis'
##   missForest iteration 1 in progress...

## randomForest 4.7-1.1

## Type rfNews() to see new features/changes/bug fixes.

## 
##  次のパッケージを付け加えます: 'randomForest'

##  以下のオブジェクトは 'package:dplyr' からマスクされています:
## 
##     combine

##  以下のオブジェクトは 'package:ggplot2' からマスクされています:
## 
##     margin

##  要求されたパッケージ rngtools をロード中です

## done!
##     estimated error(s): 0.4019074 0.02111852 
##     difference(s): 0.0004840515 0.005597015 
##     time: 1.243 seconds
## 
##   missForest iteration 2 in progress...done!
##     estimated error(s): 0.4004004 0.0207036 
##     difference(s): 0.0001136674 0.002390392 
##     time: 1.138 seconds
## 
##   missForest iteration 3 in progress...done!
##     estimated error(s): 0.3995062 0.02053885 
##     difference(s): 0.0001359508 0.002390392 
##     time: 1.12 seconds

summary(imp.mf$ximp)

##  sex     barthel child_score pad     stroke  dimentia ch_lung rheumati
##  M:405   0:193   0: 66       0:535   0:526   0:532    0:525   0:533   
##  F:131   1:154   1:291       1:  1   1: 10   1:  4    1: 11   1:  3   
##          2:189   2:179                                                
##                                                                       
##                                                                       
##                                                                       
##  pept_ulcer dm      dm_compli paralysis malignancy meta_tumor aids    eGFR30 
##  0:477      0:430   0:530     0:536     0:478      0:530      0:536   0:485  
##  1: 59      1:106   1:  6               1: 58      1:  6              1: 51  
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##  hd      hcc     alcohol past_rupture antiplate anticoag antithro nsaids 
##  0:533   0:425   0:305   0:432        0:532     0:535    0:532    0:531  
##  1:  3   1:111   1:231   1:104        1:  4     1:  1    1:  4    1:  5  
##                                                                          
##                                                                          
##                                                                          
##                                                                          
##  steroid beta    vaso    ffp     pc      albner  shock   hosp_mortality
##  0:534   0:518   0:505   0:399   0:530   0:489   0:318   0:463         
##  1:  2   1: 18   1: 31   1:137   1:  6   1: 47   1:218   1: 73         
##                                                                        
##                                                                        
##                                                                        
##                                                                        
##       age             bmi            smoke          child_num     
##  Min.   :26.00   Min.   :13.72   Min.   :   0.0   Min.   : 5.000  
##  1st Qu.:51.75   1st Qu.:20.81   1st Qu.:   0.0   1st Qu.: 7.045  
##  Median :62.00   Median :22.66   Median : 200.0   Median : 8.291  
##  Mean   :61.23   Mean   :22.90   Mean   : 301.3   Mean   : 8.805  
##  3rd Qu.:70.00   3rd Qu.:24.45   3rd Qu.: 411.6   3rd Qu.:10.000  
##  Max.   :90.00   Max.   :33.95   Max.   :9000.0   Max.   :15.000  
##       gcs           cci_num            map               bt       
##  Min.   : 3.00   Min.   : 3.000   Min.   : 0.000   Min.   :32.80  
##  1st Qu.:15.00   1st Qu.: 4.000   1st Qu.: 0.000   1st Qu.:36.30  
##  Median :15.00   Median : 4.000   Median : 4.000   Median :36.70  
##  Mean   :14.33   Mean   : 4.444   Mean   : 3.493   Mean   :36.66  
##  3rd Qu.:15.00   3rd Qu.: 5.000   3rd Qu.: 4.000   3rd Qu.:37.00  
##  Max.   :15.00   Max.   :12.000   Max.   :68.000   Max.   :41.70  
##       sBP              dBP              hr              bil         
##  Min.   : 50.00   Min.   : 3.00   Min.   : 39.00   Min.   : 0.2000  
##  1st Qu.: 79.75   1st Qu.:44.00   1st Qu.: 71.00   1st Qu.: 0.9775  
##  Median : 90.00   Median :51.00   Median : 82.00   Median : 1.4750  
##  Mean   : 87.50   Mean   :51.14   Mean   : 85.39   Mean   : 2.1906  
##  3rd Qu.: 96.00   3rd Qu.:58.00   3rd Qu.: 97.25   3rd Qu.: 2.6775  
##  Max.   :150.00   Max.   :93.00   Max.   :222.00   Max.   :15.8000  
##       ast              alt              wbc              hb        
##  Min.   : 10.00   Min.   :  7.00   Min.   : 1300   Min.   : 2.800  
##  1st Qu.: 34.00   1st Qu.: 20.00   1st Qu.: 5400   1st Qu.: 7.000  
##  Median : 58.16   Median : 30.00   Median : 7300   Median : 8.400  
##  Mean   : 85.04   Mean   : 43.35   Mean   : 8316   Mean   : 8.578  
##  3rd Qu.: 95.00   3rd Qu.: 47.00   3rd Qu.:10300   3rd Qu.:10.000  
##  Max.   :984.00   Max.   :562.00   Max.   :37700   Max.   :16.500  
##       plt              tp             alb             eGFR        
##  Min.   : 21.0   Min.   :2.200   Min.   :1.100   Min.   :  4.274  
##  1st Qu.: 73.0   1st Qu.:5.500   1st Qu.:2.500   1st Qu.: 48.745  
##  Median : 98.0   Median :6.200   Median :2.812   Median : 67.746  
##  Mean   :110.6   Mean   :6.107   Mean   :2.828   Mean   : 69.256  
##  3rd Qu.:134.2   3rd Qu.:6.635   3rd Qu.:3.200   3rd Qu.: 88.257  
##  Max.   :482.0   Max.   :9.100   Max.   :4.400   Max.   :196.029  
##       bun              cre              crp                pt        
##  Min.   :  2.90   Min.   : 0.330   Min.   : 0.0000   Min.   :  7.50  
##  1st Qu.: 16.10   1st Qu.: 0.670   1st Qu.: 0.1200   1st Qu.: 40.58  
##  Median : 23.20   Median : 0.860   Median : 0.3268   Median : 52.57  
##  Mean   : 28.34   Mean   : 1.061   Mean   : 0.7968   Mean   : 52.20  
##  3rd Qu.: 35.62   3rd Qu.: 1.140   3rd Qu.: 0.8145   3rd Qu.: 63.50  
##  Max.   :124.80   Max.   :10.950   Max.   :18.6370   Max.   :107.90  
##       aptt             los              meld      
##  Min.   : 17.60   Min.   :  0.00   Min.   : 6.00  
##  1st Qu.: 26.40   1st Qu.:  6.00   1st Qu.: 7.00  
##  Median : 28.70   Median : 10.00   Median :11.00  
##  Mean   : 32.27   Mean   : 13.77   Mean   :12.06  
##  3rd Qu.: 33.23   3rd Qu.: 18.00   3rd Qu.:15.00  
##  Max.   :240.00   Max.   :159.00   Max.   :40.00

md.pattern(imp.mf$ximp)

##  /\     /\
## {  `---'  }
## {  O   O  }
## ==>  V <==  No need for mice. This data set is completely observed.
##  \  \|/  /
##   `-----'

##     sex barthel child_score pad stroke dimentia ch_lung rheumati pept_ulcer dm
## 536   1       1           1   1      1        1       1        1          1  1
##       0       0           0   0      0        0       0        0          0  0
##     dm_compli paralysis malignancy meta_tumor aids eGFR30 hd hcc alcohol
## 536         1         1          1          1    1      1  1   1       1
##             0         0          0          0    0      0  0   0       0
##     past_rupture antiplate anticoag antithro nsaids steroid beta vaso ffp pc
## 536            1         1        1        1      1       1    1    1   1  1
##                0         0        0        0      0       0    0    0   0  0
##     albner shock hosp_mortality age bmi smoke child_num gcs cci_num map bt sBP
## 536      1     1              1   1   1     1         1   1       1   1  1   1
##          0     0              0   0   0     0         0   0       0   0  0   0
##     dBP hr bil ast alt wbc hb plt tp alb eGFR bun cre crp pt aptt los meld  
## 536   1  1   1   1   1   1  1   1  1   1    1   1   1   1  1    1   1    1 0
##       0  0   0   0   0   0  0   0  0   0    0   0   0   0  0    0   0    0 0

#if exclude ID is necessary add the ID
data_imp <- imp.mf$ximp
data_imp$pt_id <- df_dev$pt_id
data_imp$hosp_num<- df_dev$hosp_num
data_imp$hosp_id<- df_dev$hosp_id
data_imp$year<- df_dev$year
#check
str(data_imp)

## 'data.frame':    536 obs. of  63 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 1 2 1 2 1 2 1 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": 3 3 1 2 3 1 1 1 3 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 1 2 2 2 2 2 2 3 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 2 1 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 1 2 2 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 1 2 2 2 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 2 ...
##  $ age           : num  50 80 59 44 67 65 49 73 69 62 ...
##  $ bmi           : num  23.4 25.3 23.5 14.5 21.9 ...
##  $ smoke         : num  0 0 0 240 0 ...
##  $ child_num     : num  11 6 8.75 8 7.55 ...
##  $ gcs           : num  15 15 15 15 15 15 15 15 6 15 ...
##  $ cci_num       : num  4 4 3 4 4 3 4 4 4 4 ...
##  $ map           : num  0 2 0 6 2 2 0 4 14 6 ...
##  $ bt            : num  36.4 36.8 35.9 36 36.6 38.4 37 37 35.5 36.6 ...
##  $ sBP           : num  78 88 100 69 66 84 132 90 52 58 ...
##  $ dBP           : num  48 49 56 44 40 46 69 54 37 37 ...
##  $ hr            : num  118 72 110 104 72 127 83 114 98 106 ...
##  $ bil           : num  2.2 1.2 3.1 3.4 1.2 2.4 1.2 2.2 8.7 2.1 ...
##  $ ast           : num  217 31 60 129 52 90 154 55 96 121 ...
##  $ alt           : num  63 22 40 46 36 19 109 20 87 63 ...
##  $ wbc           : num  7400 5000 7800 9100 3900 8000 7900 11100 12800 8300 ...
##  $ hb            : num  6.9 10.8 9.7 10.7 6.3 9.8 13.5 5.6 6 9.8 ...
##  $ plt           : num  115 77 74 162 63 93 132 124 168 84 ...
##  $ tp            : num  6.3 5.6 6.4 6.1 5.1 7.2 7.6 4.9 5.3 6.3 ...
##  $ alb           : num  2.2 3.2 2.8 2.9 2.8 3.2 4 2.3 1.2 2.5 ...
##  $ eGFR          : num  58 57.7 63.7 112.4 123.6 ...
##  $ bun           : num  13.2 41.5 26.3 2.9 27.4 15.8 15.5 27 63.2 13.8 ...
##  $ cre           : num  1.08 0.96 0.72 0.61 0.38 0.44 0.4 0.97 2.07 0.87 ...
##  $ crp           : num  0.92 0.29 0.68 0.29 0.29 ...
##  $ pt            : num  37.8 55 46.7 37.8 74.6 45.9 49.7 45.9 37.2 54 ...
##  $ aptt          : num  29.4 29 27.2 35.3 30.1 27.6 32.6 27.5 36 29.1 ...
##  $ los           : num  12 7 0 10 3 2 1 8 0 16 ...
##  $ meld          : num  16 11 13 14 6 8 6 15 29 12 ...
##  $ pt_id         : int  1 2 3 4 5 7 8 9 10 11 ...
##  $ hosp_num      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_id       : int  1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
##  $ year          : int  2012 2011 2010 2011 2010 2010 2010 2010 2011 2012 ...

#write the csv
#write.csv(imp.mf$ximp, file = "data_after_imputation.csv")

データの確認 meldを丸める

str(data_imp)

## 'data.frame':    536 obs. of  63 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 1 2 1 2 1 2 1 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": 3 3 1 2 3 1 1 1 3 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 1 2 2 2 2 2 2 3 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 2 1 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 1 2 2 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 1 2 2 2 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 2 ...
##  $ age           : num  50 80 59 44 67 65 49 73 69 62 ...
##  $ bmi           : num  23.4 25.3 23.5 14.5 21.9 ...
##  $ smoke         : num  0 0 0 240 0 ...
##  $ child_num     : num  11 6 8.75 8 7.55 ...
##  $ gcs           : num  15 15 15 15 15 15 15 15 6 15 ...
##  $ cci_num       : num  4 4 3 4 4 3 4 4 4 4 ...
##  $ map           : num  0 2 0 6 2 2 0 4 14 6 ...
##  $ bt            : num  36.4 36.8 35.9 36 36.6 38.4 37 37 35.5 36.6 ...
##  $ sBP           : num  78 88 100 69 66 84 132 90 52 58 ...
##  $ dBP           : num  48 49 56 44 40 46 69 54 37 37 ...
##  $ hr            : num  118 72 110 104 72 127 83 114 98 106 ...
##  $ bil           : num  2.2 1.2 3.1 3.4 1.2 2.4 1.2 2.2 8.7 2.1 ...
##  $ ast           : num  217 31 60 129 52 90 154 55 96 121 ...
##  $ alt           : num  63 22 40 46 36 19 109 20 87 63 ...
##  $ wbc           : num  7400 5000 7800 9100 3900 8000 7900 11100 12800 8300 ...
##  $ hb            : num  6.9 10.8 9.7 10.7 6.3 9.8 13.5 5.6 6 9.8 ...
##  $ plt           : num  115 77 74 162 63 93 132 124 168 84 ...
##  $ tp            : num  6.3 5.6 6.4 6.1 5.1 7.2 7.6 4.9 5.3 6.3 ...
##  $ alb           : num  2.2 3.2 2.8 2.9 2.8 3.2 4 2.3 1.2 2.5 ...
##  $ eGFR          : num  58 57.7 63.7 112.4 123.6 ...
##  $ bun           : num  13.2 41.5 26.3 2.9 27.4 15.8 15.5 27 63.2 13.8 ...
##  $ cre           : num  1.08 0.96 0.72 0.61 0.38 0.44 0.4 0.97 2.07 0.87 ...
##  $ crp           : num  0.92 0.29 0.68 0.29 0.29 ...
##  $ pt            : num  37.8 55 46.7 37.8 74.6 45.9 49.7 45.9 37.2 54 ...
##  $ aptt          : num  29.4 29 27.2 35.3 30.1 27.6 32.6 27.5 36 29.1 ...
##  $ los           : num  12 7 0 10 3 2 1 8 0 16 ...
##  $ meld          : num  16 11 13 14 6 8 6 15 29 12 ...
##  $ pt_id         : int  1 2 3 4 5 7 8 9 10 11 ...
##  $ hosp_num      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_id       : int  1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
##  $ year          : int  2012 2011 2010 2011 2010 2010 2010 2010 2011 2012 ...

data_imp$meld <- round(data_imp$meld)
min(data_imp$meld, na.rm = TRUE)

## [1] 6

max(data_imp$meld, na.rm = TRUE)

## [1] 40

データclass調整

dev_imp <-
  data_imp|> 　
  mutate(
        hosp_id=as.integer(hosp_id),
        pt_id=as.integer(pt_id),
        hosp_num=as.integer(hosp_num),
        year=as.integer(year),
        age=as.integer(age),
        sex= factor(sex, levels = c("M", "F")),
        smoke= as.integer(smoke),
        barthel= factor(barthel, levels = c("0", "1", "2")),
        child_num= as.integer(round(data_imp$child_num)),
        child_score=factor(child_score, levels = c("0", "1", "2")),
        gcs=as.integer(gcs),
        cci_num=as.integer(cci_num),
        pad=factor(pad),
        stroke=factor(stroke),
        dimentia=factor(dimentia),
        ch_lung=factor(ch_lung),
        rheumati=factor(rheumati),
        pept_ulcer=factor(pept_ulcer),
        dm=factor(dm),
        dm_compli=factor(dm_compli),
        paralysis=factor(paralysis),
        malignancy=factor(malignancy),
        meta_tumor=factor(meta_tumor),
        aids=factor(aids),
        eGFR30=factor(eGFR30),
        hd=factor(hd),
        hcc=factor(hcc),
        alcohol=factor(alcohol),
        past_rupture=factor(past_rupture),
        antiplate=factor(antiplate),
        anticoag=factor(anticoag),
        antithro=factor(antithro),
        nsaids=factor(nsaids),
        steroid=factor(steroid),
        beta=factor(beta),
        vaso=factor(vaso),
        map= as.integer(map),
        ffp=factor(ffp),
        pc=factor(pc),
        albner=factor(albner),
        sBP= as.integer(sBP),
        dBP= as.integer(dBP),
        hr=as.integer(hr),
        shock=factor(shock),
        los=as.integer(los),
        meld=as.integer(meld)
          )

dev_imp$child_numが整数になっているかの確認

table(dev_imp$child_num)

## 
##   5   6   7   8   9  10  11  12  13  14  15 
##  20  46  73 137  76  71  47  41  18   3   4

作成データの確認

dfSummary(dev_imp) %>% view()

## Switching method to 'browser'

## Output file written: /var/folders/n9/tf_wmwpn3gl2t7l1cz4tqk000000gn/T//RtmpVgA1vn/file965d51b6c687.html

str(dev_imp)

## 'data.frame':    536 obs. of  63 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 1 2 1 2 1 2 1 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": 3 3 1 2 3 1 1 1 3 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 1 2 2 2 2 2 2 3 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 2 1 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 1 2 2 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 1 2 2 2 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 2 ...
##  $ age           : int  50 80 59 44 67 65 49 73 69 62 ...
##  $ bmi           : num  23.4 25.3 23.5 14.5 21.9 ...
##  $ smoke         : int  0 0 0 240 0 0 217 1000 0 0 ...
##  $ child_num     : int  11 6 9 8 8 8 7 9 15 11 ...
##  $ gcs           : int  15 15 15 15 15 15 15 15 6 15 ...
##  $ cci_num       : int  4 4 3 4 4 3 4 4 4 4 ...
##  $ map           : int  0 2 0 6 2 2 0 4 14 6 ...
##  $ bt            : num  36.4 36.8 35.9 36 36.6 38.4 37 37 35.5 36.6 ...
##  $ sBP           : int  78 88 100 69 66 84 132 90 52 58 ...
##  $ dBP           : int  48 49 56 44 40 46 69 54 37 37 ...
##  $ hr            : int  118 72 110 104 72 127 83 114 98 106 ...
##  $ bil           : num  2.2 1.2 3.1 3.4 1.2 2.4 1.2 2.2 8.7 2.1 ...
##  $ ast           : num  217 31 60 129 52 90 154 55 96 121 ...
##  $ alt           : num  63 22 40 46 36 19 109 20 87 63 ...
##  $ wbc           : num  7400 5000 7800 9100 3900 8000 7900 11100 12800 8300 ...
##  $ hb            : num  6.9 10.8 9.7 10.7 6.3 9.8 13.5 5.6 6 9.8 ...
##  $ plt           : num  115 77 74 162 63 93 132 124 168 84 ...
##  $ tp            : num  6.3 5.6 6.4 6.1 5.1 7.2 7.6 4.9 5.3 6.3 ...
##  $ alb           : num  2.2 3.2 2.8 2.9 2.8 3.2 4 2.3 1.2 2.5 ...
##  $ eGFR          : num  58 57.7 63.7 112.4 123.6 ...
##  $ bun           : num  13.2 41.5 26.3 2.9 27.4 15.8 15.5 27 63.2 13.8 ...
##  $ cre           : num  1.08 0.96 0.72 0.61 0.38 0.44 0.4 0.97 2.07 0.87 ...
##  $ crp           : num  0.92 0.29 0.68 0.29 0.29 ...
##  $ pt            : num  37.8 55 46.7 37.8 74.6 45.9 49.7 45.9 37.2 54 ...
##  $ aptt          : num  29.4 29 27.2 35.3 30.1 27.6 32.6 27.5 36 29.1 ...
##  $ los           : int  12 7 0 10 3 2 1 8 0 16 ...
##  $ meld          : int  16 11 13 14 6 8 6 15 29 12 ...
##  $ pt_id         : int  1 2 3 4 5 7 8 9 10 11 ...
##  $ hosp_num      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_id       : int  1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
##  $ year          : int  2012 2011 2010 2011 2010 2010 2010 2010 2011 2012 ...

代入後、開発コホートtableone作成

# Create your table
dev_imp %>%  
  select(c(col_cont, col_fact)) %>% 
  CreateTableOne(vars = c(col_cont, col_fact), strata="hosp_mortality",factorVars = col_fact, addOverall = T) -> tableone_dev_imp


# Print your table
print(tableone_dev_imp, smd = TRUE, missing = TRUE, test = TRUE, explain = TRUE) 

##                         Stratified by hosp_mortality
##                          Overall           0                 1                
##   n                          536               463                73          
##   age (mean (SD))          61.23 (13.06)     60.82 (13.02)     63.84 (13.09)  
##   bmi (mean (SD))          22.90 (3.43)      22.99 (3.48)      22.28 (3.04)   
##   smoke (mean (SD))       301.23 (528.30)   285.63 (366.19)   400.16 (1096.34)
##   child_num (mean (SD))     8.81 (2.06)       8.48 (1.87)      10.90 (2.01)   
##   gcs (mean (SD))          14.33 (2.20)      14.72 (1.27)      11.90 (4.33)   
##   cci_num (mean (SD))       4.44 (1.20)       4.43 (1.08)       4.53 (1.78)   
##   map (mean (SD))           3.49 (4.51)       3.10 (4.35)       5.96 (4.71)   
##   bt (mean (SD))           36.66 (0.74)      36.68 (0.65)      36.49 (1.16)   
##   sBP (mean (SD))          87.48 (15.26)     90.26 (13.37)     69.85 (14.79)  
##   dBP (mean (SD))          51.12 (11.71)     52.94 (10.80)     39.56 (10.65)  
##   hr (mean (SD))           85.37 (20.73)     82.95 (18.82)    100.68 (25.41)  
##   bil (mean (SD))           2.19 (2.03)       1.96 (1.76)       3.66 (2.85)   
##   ast (mean (SD))          85.04 (91.05)     79.34 (86.79)    121.21 (108.33) 
##   alt (mean (SD))          43.35 (50.11)     41.69 (49.31)     53.91 (54.05)  
##   wbc (mean (SD))        8315.81 (4354.87) 8160.66 (4158.83) 9299.86 (5366.73)
##   hb (mean (SD))            8.58 (2.44)       8.65 (2.45)       8.11 (2.38)   
##   plt (mean (SD))         110.59 (57.13)    109.02 (51.35)    120.60 (84.92)  
##   tp (mean (SD))            6.11 (0.89)       6.16 (0.83)       5.78 (1.15)   
##   alb (mean (SD))           2.83 (0.58)       2.91 (0.53)       2.31 (0.62)   
##   eGFR (mean (SD))         69.26 (30.44)     72.87 (30.10)     46.36 (21.32)  
##   bun (mean (SD))          28.34 (18.43)     27.43 (17.57)     34.11 (22.48)  
##   cre (mean (SD))           1.06 (0.91)       1.00 (0.90)       1.46 (0.82)   
##   crp (mean (SD))           0.80 (1.61)       0.70 (1.54)       1.44 (1.90)   
##   pt (mean (SD))           52.20 (16.66)     53.87 (15.65)     41.59 (18.93)  
##   aptt (mean (SD))         32.27 (16.38)     30.66 (11.58)     42.45 (31.81)  
##   los (mean (SD))          13.77 (15.28)     14.21 (14.24)     11.01 (20.59)  
##   meld (mean (SD))         12.07 (6.01)      10.98 (4.74)      18.93 (8.29)   
##   sex = F (%)                131 ( 24.4)       111 ( 24.0)        20 ( 27.4)  
##   barthel (%)                                                                 
##      0                       193 ( 36.0)       186 ( 40.2)         7 (  9.6)  
##      1                       154 ( 28.7)       142 ( 30.7)        12 ( 16.4)  
##      2                       189 ( 35.3)       135 ( 29.2)        54 ( 74.0)  
##   child_score (%)                                                             
##      0                        66 ( 12.3)        65 ( 14.0)         1 (  1.4)  
##      1                       291 ( 54.3)       271 ( 58.5)        20 ( 27.4)  
##      2                       179 ( 33.4)       127 ( 27.4)        52 ( 71.2)  
##   pad = 1 (%)                  1 (  0.2)         1 (  0.2)         0 (  0.0)  
##   stroke = 1 (%)              10 (  1.9)        10 (  2.2)         0 (  0.0)  
##   dimentia = 1 (%)             4 (  0.7)         4 (  0.9)         0 (  0.0)  
##   ch_lung = 1 (%)             11 (  2.1)         9 (  1.9)         2 (  2.7)  
##   rheumati = 1 (%)             3 (  0.6)         3 (  0.6)         0 (  0.0)  
##   pept_ulcer = 1 (%)          59 ( 11.0)        58 ( 12.5)         1 (  1.4)  
##   dm = 1 (%)                 106 ( 19.8)        98 ( 21.2)         8 ( 11.0)  
##   dm_compli = 1 (%)            6 (  1.1)         6 (  1.3)         0 (  0.0)  
##   paralysis = 0 (%)          536 (100.0)       463 (100.0)        73 (100.0)  
##   malignancy = 1 (%)          58 ( 10.8)        46 (  9.9)        12 ( 16.4)  
##   meta_tumor = 1 (%)           6 (  1.1)         2 (  0.4)         4 (  5.5)  
##   aids = 0 (%)               536 (100.0)       463 (100.0)        73 (100.0)  
##   eGFR30 = 1 (%)              51 (  9.5)        34 (  7.3)        17 ( 23.3)  
##   hd = 1 (%)                   3 (  0.6)         3 (  0.6)         0 (  0.0)  
##   hcc = 1 (%)                111 ( 20.7)        95 ( 20.5)        16 ( 21.9)  
##   alcohol = 1 (%)            231 ( 43.1)       205 ( 44.3)        26 ( 35.6)  
##   past_rupture = 1 (%)       104 ( 19.4)        96 ( 20.7)         8 ( 11.0)  
##   antiplate = 1 (%)            4 (  0.7)         3 (  0.6)         1 (  1.4)  
##   anticoag = 1 (%)             1 (  0.2)         1 (  0.2)         0 (  0.0)  
##   antithro = 1 (%)             4 (  0.7)         3 (  0.6)         1 (  1.4)  
##   nsaids = 1 (%)               5 (  0.9)         5 (  1.1)         0 (  0.0)  
##   steroid = 1 (%)              2 (  0.4)         1 (  0.2)         1 (  1.4)  
##   beta = 1 (%)                18 (  3.4)        18 (  3.9)         0 (  0.0)  
##   vaso = 1 (%)                31 (  5.8)        11 (  2.4)        20 ( 27.4)  
##   ffp = 1 (%)                137 ( 25.6)       100 ( 21.6)        37 ( 50.7)  
##   pc = 1 (%)                   6 (  1.1)         3 (  0.6)         3 (  4.1)  
##   albner = 1 (%)              47 (  8.8)        32 (  6.9)        15 ( 20.5)  
##   shock = 1 (%)              218 ( 40.7)       156 ( 33.7)        62 ( 84.9)  
##   hosp_mortality = 1 (%)      73 ( 13.6)         0 (  0.0)        73 (100.0)  
##                         Stratified by hosp_mortality
##                          p      test SMD    Missing
##   n                                                
##   age (mean (SD))         0.066       0.231 0.0    
##   bmi (mean (SD))         0.102       0.216 0.0    
##   smoke (mean (SD))       0.085       0.140 0.0    
##   child_num (mean (SD))  <0.001       1.249 0.0    
##   gcs (mean (SD))        <0.001       0.882 0.0    
##   cci_num (mean (SD))     0.490       0.071 0.0    
##   map (mean (SD))        <0.001       0.630 0.0    
##   bt (mean (SD))          0.041       0.203 0.0    
##   sBP (mean (SD))        <0.001       1.448 0.0    
##   dBP (mean (SD))        <0.001       1.247 0.0    
##   hr (mean (SD))         <0.001       0.793 0.0    
##   bil (mean (SD))        <0.001       0.717 0.0    
##   ast (mean (SD))        <0.001       0.427 0.0    
##   alt (mean (SD))         0.053       0.236 0.0    
##   wbc (mean (SD))         0.038       0.237 0.0    
##   hb (mean (SD))          0.078       0.225 0.0    
##   plt (mean (SD))         0.107       0.165 0.0    
##   tp (mean (SD))          0.001       0.377 0.0    
##   alb (mean (SD))        <0.001       1.033 0.0    
##   eGFR (mean (SD))       <0.001       1.016 0.0    
##   bun (mean (SD))         0.004       0.331 0.0    
##   cre (mean (SD))        <0.001       0.541 0.0    
##   crp (mean (SD))        <0.001       0.431 0.0    
##   pt (mean (SD))         <0.001       0.707 0.0    
##   aptt (mean (SD))       <0.001       0.493 0.0    
##   los (mean (SD))         0.097       0.180 0.0    
##   meld (mean (SD))       <0.001       1.177 0.0    
##   sex = F (%)             0.627       0.078 0.0    
##   barthel (%)            <0.001       1.040 0.0    
##      0                                             
##      1                                             
##      2                                             
##   child_score (%)        <0.001       1.018 0.0    
##      0                                             
##      1                                             
##      2                                             
##   pad = 1 (%)             1.000       0.066 0.0    
##   stroke = 1 (%)          0.422       0.210 0.0    
##   dimentia = 1 (%)        0.948       0.132 0.0    
##   ch_lung = 1 (%)         0.999       0.053 0.0    
##   rheumati = 1 (%)        1.000       0.114 0.0    
##   pept_ulcer = 1 (%)      0.009       0.450 0.0    
##   dm = 1 (%)              0.061       0.281 0.0    
##   dm_compli = 1 (%)       0.704       0.162 0.0    
##   paralysis = 0 (%)          NA      <0.001 0.0    
##   malignancy = 1 (%)      0.144       0.193 0.0    
##   meta_tumor = 1 (%)      0.001       0.301 0.0    
##   aids = 0 (%)               NA      <0.001 0.0    
##   eGFR30 = 1 (%)         <0.001       0.454 0.0    
##   hd = 1 (%)              1.000       0.114 0.0    
##   hcc = 1 (%)             0.905       0.034 0.0    
##   alcohol = 1 (%)         0.207       0.178 0.0    
##   past_rupture = 1 (%)    0.071       0.270 0.0    
##   antiplate = 1 (%)       1.000       0.072 0.0    
##   anticoag = 1 (%)        1.000       0.066 0.0    
##   antithro = 1 (%)        1.000       0.072 0.0    
##   nsaids = 1 (%)          0.813       0.148 0.0    
##   steroid = 1 (%)         0.638       0.130 0.0    
##   beta = 1 (%)            0.173       0.284 0.0    
##   vaso = 1 (%)           <0.001       0.751 0.0    
##   ffp = 1 (%)            <0.001       0.635 0.0    
##   pc = 1 (%)              0.044       0.229 0.0    
##   albner = 1 (%)         <0.001       0.404 0.0    
##   shock = 1 (%)          <0.001       1.222 0.0    
##   hosp_mortality = 1 (%) <0.001         NaN 0.0

代入後の開発データでもtbl_summaryでmedian+IQRも準備する

# specify your data and variables
tbl_summary(data = dev_imp, 
            by = "hosp_mortality",
            type = list(gcs ~ "continuous", year ~ "categorical"),
            statistic = all_continuous() ~ "{median} ({p25}, {p75})",
            digits = all_continuous() ~ c(0, 2))

Characteristic	0, N = 4631	1, N = 731
sex
M	352 (76%)	53 (73%)
F	111 (24%)	20 (27%)
barthel
0	186 (40%)	7 (9.6%)
1	142 (31%)	12 (16%)
2	135 (29%)	54 (74%)
child_score
0	65 (14%)	1 (1.4%)
1	271 (59%)	20 (27%)
2	127 (27%)	52 (71%)
pad
0	462 (100%)	73 (100%)
1	1 (0.2%)	0 (0%)
stroke
0	453 (98%)	73 (100%)
1	10 (2.2%)	0 (0%)
dimentia
0	459 (99%)	73 (100%)
1	4 (0.9%)	0 (0%)
ch_lung
0	454 (98%)	71 (97%)
1	9 (1.9%)	2 (2.7%)
rheumati
0	460 (99%)	73 (100%)
1	3 (0.6%)	0 (0%)
pept_ulcer
0	405 (87%)	72 (99%)
1	58 (13%)	1 (1.4%)
dm
0	365 (79%)	65 (89%)
1	98 (21%)	8 (11%)
dm_compli
0	457 (99%)	73 (100%)
1	6 (1.3%)	0 (0%)
paralysis
0	463 (100%)	73 (100%)
malignancy
0	417 (90%)	61 (84%)
1	46 (9.9%)	12 (16%)
meta_tumor
0	461 (100%)	69 (95%)
1	2 (0.4%)	4 (5.5%)
aids
0	463 (100%)	73 (100%)
eGFR30
0	429 (93%)	56 (77%)
1	34 (7.3%)	17 (23%)
hd
0	460 (99%)	73 (100%)
1	3 (0.6%)	0 (0%)
hcc
0	368 (79%)	57 (78%)
1	95 (21%)	16 (22%)
alcohol
0	258 (56%)	47 (64%)
1	205 (44%)	26 (36%)
past_rupture
0	367 (79%)	65 (89%)
1	96 (21%)	8 (11%)
antiplate
0	460 (99%)	72 (99%)
1	3 (0.6%)	1 (1.4%)
anticoag
0	462 (100%)	73 (100%)
1	1 (0.2%)	0 (0%)
antithro
0	460 (99%)	72 (99%)
1	3 (0.6%)	1 (1.4%)
nsaids
0	458 (99%)	73 (100%)
1	5 (1.1%)	0 (0%)
steroid
0	462 (100%)	72 (99%)
1	1 (0.2%)	1 (1.4%)
beta
0	445 (96%)	73 (100%)
1	18 (3.9%)	0 (0%)
vaso
0	452 (98%)	53 (73%)
1	11 (2.4%)	20 (27%)
ffp
0	363 (78%)	36 (49%)
1	100 (22%)	37 (51%)
pc
0	460 (99%)	70 (96%)
1	3 (0.6%)	3 (4.1%)
albner
0	431 (93%)	58 (79%)
1	32 (6.9%)	15 (21%)
shock
0	307 (66%)	11 (15%)
1	156 (34%)	62 (85%)
age	62 (51.00, 70)	66 (55.00, 74)
bmi	23 (20.81, 25)	23 (20.96, 24)
smoke	200 (0.00, 413)	180 (0.00, 400)
child_num	8 (7.00, 10)	11 (9.00, 12)
gcs	15 (15.00, 15)	15 (9.00, 15)
cci_num	4 (4.00, 5)	4 (4.00, 5)
map	4 (0.00, 4)	6 (4.00, 8)
bt	37 (36.30, 37)	36 (36.00, 37)
sBP	90 (82.00, 98)	65 (59.00, 79)
dBP	53 (46.00, 59)	40 (33.00, 46)
hr	80 (70.00, 93)	101 (89.00, 112)
bil	1 (0.90, 2)	3 (1.66, 5)
ast	55 (33.00, 90)	84 (49.00, 153)
alt	29 (20.00, 44)	33 (25.00, 63)
wbc	7,200 (5,315.00, 10,240)	7,990 (6,200.00, 10,800)
hb	8 (7.00, 10)	8 (6.60, 10)
plt	98 (73.00, 135)	98 (76.00, 130)
tp	6 (5.60, 7)	6 (5.20, 6)
alb	3 (2.60, 3)	2 (1.80, 3)
eGFR	71 (52.42, 92)	44 (30.40, 58)
bun	23 (15.50, 35)	28 (18.10, 46)
cre	1 (0.66, 1)	1 (0.99, 2)
crp	0 (0.11, 1)	1 (0.40, 2)
pt	54 (43.00, 64)	40 (26.80, 55)
aptt	28 (26.05, 32)	32 (29.10, 44)
los	10 (7.00, 18)	4 (1.00, 16)
meld	10 (7.00, 14)	16 (13.00, 25)
pt_id	359 (149.50, 583)	426 (235.00, 599)
hosp_num
1	404 (87%)	68 (93%)
2	43 (9.3%)	5 (6.8%)
3	15 (3.2%)	0 (0%)
4	1 (0.2%)	0 (0%)
hosp_id	1,010 (1,003.00, 1,024)	1,017 (1,005.00, 1,024)
year
2010	68 (15%)	8 (11%)
2011	70 (15%)	9 (12%)
2012	72 (16%)	16 (22%)
2013	55 (12%)	15 (21%)
2014	69 (15%)	8 (11%)
2015	67 (14%)	5 (6.8%)
2016	62 (13%)	12 (16%)
1 n (%); Median (IQR)

開発コホート 連続量可視化

dev_imp |> 　#全体
  select(col_continuous) |> 
  pivot_longer(cols = col_continuous, names_to = "name", values_to = "value") |> 
  ggplot()+
  geom_histogram(aes(x = value), color = "black")+
  facet_wrap(~ name, scales = "free", ncol = 5) +
  theme_bw()+
  theme(text = element_text(size = 12))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

# 連続変数の変数名をまとめる
con_var <- c("age", "bmi","smoke","child_num","gcs","cci_num","map","bt","sBP","dBP","hr","bil","ast","alt","wbc","hb","plt","tp","alb","eGFR","bun","cre","crp","pt","aptt","meld")

# datadistを計算
ddist <- datadist(dev_imp)
options(datadist='ddist')

# プロット結果をまとめるリストを用意する
plot <- list()

for (x in con_var){
  # lrmに投入するformulaを文字列で、for文で順番に指定していく
  formula_tmp <- as.formula(paste("hosp_mortality ~ rcs(", x, ", 4)"))
  fit_tmp <- lrm(formula_tmp, data = dev_imp)
  
  # Predict関数の呼び出しを文字列として作成し、それをパースして評価する
  plot_cmd <- paste("plot(Predict(fit_tmp, ", x, "))")
  plot_tmp <- eval(parse(text = plot_cmd))
  plot[[x]] <- plot_tmp
}

# 結果をggarrangeでまとめて表示する
logOR_plot <- ggarrange(plotlist = plot, ncol = 1, nrow = 1)

logOR_plot

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## 
## attr(,"class")
## [1] "list"      "ggarrange"

以下のCut offを設ける ・age:60以上・未満　60未満:0, 60以上:1 ・bmi:25以上・未満 25未満:0, 25以上:1 ・smoke:cut offなし →むしろ変数から削除 ・child_numl：cut offなし→むしろ変数から削除 ・gcs：12以上・未満　12未満:0, 12以上:1 ・cci：6点以上・未満 6未満:0, 6以上:1 ・bt:37度以上・未満 37度未満:0,37度以上：1 ・sBP:80以上・未満 80以上:0,80未満：1 ・dBP:50以上・未満 50以上:0,50未満：1 ・HR:100以上・未満 100未満：0,100以上：1 ・bil:5以上・未満： 5未満：0,5以上：1 ・ast:200以上・未満 200未満：0, 200以上:1 ・alt:100以上・未満 100未満：0, 100以上：1 ・wbc:12000以上・未満 12000未満：0, 12000以上：1　 ・hb:8以上・未満　9以上：0, 8未満：1 ・plt:100以上・未満　100以上：0, 100未満：1　 ・tp:6以上・未満 6以上：0, 6未満：1 ・alb:2.8以上・未満 2.8以上：0, 2.8未満：1 ・Cre:1.5以上・未満 1.5未満：0, Cre1.5以上：1 ・CRP:2以上・未満　2未満：0, CRP2以上：1 ・pt:50以上・未満　50以上：0, 50未満：1 ・aptt:50以上・未満 aptt50未満：0,50以上：1

上記を目安に新しく列を追加

#新規カテゴリ列を作成
dev_imp$age_cate <- ifelse(dev_imp$age >= 60, 1, 0)
dev_imp$bmi_cate <- ifelse(dev_imp$bmi >= 25, 1, 0) # bmiは25以上：1, 25未満：0
dev_imp$gcs_cate <- ifelse(dev_imp$gcs < 12, 1, 0)
dev_imp$cci_cate <- ifelse(dev_imp$cci_num >= 6, 1, 0)
dev_imp$bt_cate <- ifelse(dev_imp$bt >= 37, 1, 0)
dev_imp$sBP_cate <- ifelse(dev_imp$sBP < 80, 1, 0)
dev_imp$dBP_cate <- ifelse(dev_imp$dBP < 50, 1, 0)
dev_imp$hr_cate <- ifelse(dev_imp$hr >= 100, 1, 0)
dev_imp$bil_cate <- ifelse(dev_imp$bil >= 5, 1, 0)
dev_imp$ast_cate <- ifelse(dev_imp$ast >= 200, 1, 0)
dev_imp$alt_cate <- ifelse(dev_imp$alt >= 100, 1, 0)
dev_imp$wbc_cate <- ifelse(dev_imp$wbc >= 12000, 1, 0)
dev_imp$hb_cate <- ifelse(dev_imp$hb < 8, 1, 0)
dev_imp$plt_cate <- ifelse(dev_imp$plt < 100, 1, 0) # pltは100未満：1、100以上：0
dev_imp$tp_cate <- ifelse(dev_imp$tp < 6, 1, 0)
dev_imp$alb_cate <- ifelse(dev_imp$alb < 2.8, 1, 0)
dev_imp$cre_cate <- ifelse(dev_imp$cre >= 1.5, 1, 0)
dev_imp$crp_cate <- ifelse(dev_imp$crp >= 2, 1, 0)
dev_imp$pt_cate <- ifelse(dev_imp$pt < 50, 1, 0)
dev_imp$aptt_cate <- ifelse(dev_imp$aptt >= 50, 1, 0) # apttは50以上：1, 50未満：0

カテゴリー変換後のtable1作成

Createtableone

col_fact_cate=c("sex","barthel","child_score","pad","stroke","dimentia","ch_lung","rheumati","pept_ulcer","dm","dm_compli","paralysis","malignancy","meta_tumor","aids","eGFR30","hd","hcc","alcohol","past_rupture","antiplate","anticoag","antithro","nsaids","steroid","beta", "vaso","ffp","pc", "albner","shock","hosp_mortality","age_cate","bmi_cate","gcs_cate","cci_cate","bt_cate","sBP_cate","dBP_cate","hr_cate","bil_cate","ast_cate","alt_cate","wbc_cate","hb_cate","plt_cate","tp_cate","alb_cate","cre_cate","crp_cate","pt_cate","aptt_cate")

# Create your table
dev_imp %>%  
  select(c(col_fact_cate)) %>% 
  CreateTableOne(vars = c(col_fact_cate), strata="hosp_mortality",factorVars = col_fact_cate, addOverall = T) -> tableone_dev_imp_cate

## Warning: Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
##   # Was:
##   data %>% select(col_fact_cate)
## 
##   # Now:
##   data %>% select(all_of(col_fact_cate))
## 
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

# Print your table
print(tableone_dev_imp_cate, smd = TRUE, missing = TRUE, test = TRUE, explain = TRUE) 

##                         Stratified by hosp_mortality
##                          Overall      0            1           p      test
##   n                      536          463          73                     
##   sex = F (%)            131 ( 24.4)  111 ( 24.0)  20 ( 27.4)   0.627     
##   barthel (%)                                                  <0.001     
##      0                   193 ( 36.0)  186 ( 40.2)   7 (  9.6)             
##      1                   154 ( 28.7)  142 ( 30.7)  12 ( 16.4)             
##      2                   189 ( 35.3)  135 ( 29.2)  54 ( 74.0)             
##   child_score (%)                                              <0.001     
##      0                    66 ( 12.3)   65 ( 14.0)   1 (  1.4)             
##      1                   291 ( 54.3)  271 ( 58.5)  20 ( 27.4)             
##      2                   179 ( 33.4)  127 ( 27.4)  52 ( 71.2)             
##   pad = 1 (%)              1 (  0.2)    1 (  0.2)   0 (  0.0)   1.000     
##   stroke = 1 (%)          10 (  1.9)   10 (  2.2)   0 (  0.0)   0.422     
##   dimentia = 1 (%)         4 (  0.7)    4 (  0.9)   0 (  0.0)   0.948     
##   ch_lung = 1 (%)         11 (  2.1)    9 (  1.9)   2 (  2.7)   0.999     
##   rheumati = 1 (%)         3 (  0.6)    3 (  0.6)   0 (  0.0)   1.000     
##   pept_ulcer = 1 (%)      59 ( 11.0)   58 ( 12.5)   1 (  1.4)   0.009     
##   dm = 1 (%)             106 ( 19.8)   98 ( 21.2)   8 ( 11.0)   0.061     
##   dm_compli = 1 (%)        6 (  1.1)    6 (  1.3)   0 (  0.0)   0.704     
##   paralysis = 0 (%)      536 (100.0)  463 (100.0)  73 (100.0)      NA     
##   malignancy = 1 (%)      58 ( 10.8)   46 (  9.9)  12 ( 16.4)   0.144     
##   meta_tumor = 1 (%)       6 (  1.1)    2 (  0.4)   4 (  5.5)   0.001     
##   aids = 0 (%)           536 (100.0)  463 (100.0)  73 (100.0)      NA     
##   eGFR30 = 1 (%)          51 (  9.5)   34 (  7.3)  17 ( 23.3)  <0.001     
##   hd = 1 (%)               3 (  0.6)    3 (  0.6)   0 (  0.0)   1.000     
##   hcc = 1 (%)            111 ( 20.7)   95 ( 20.5)  16 ( 21.9)   0.905     
##   alcohol = 1 (%)        231 ( 43.1)  205 ( 44.3)  26 ( 35.6)   0.207     
##   past_rupture = 1 (%)   104 ( 19.4)   96 ( 20.7)   8 ( 11.0)   0.071     
##   antiplate = 1 (%)        4 (  0.7)    3 (  0.6)   1 (  1.4)   1.000     
##   anticoag = 1 (%)         1 (  0.2)    1 (  0.2)   0 (  0.0)   1.000     
##   antithro = 1 (%)         4 (  0.7)    3 (  0.6)   1 (  1.4)   1.000     
##   nsaids = 1 (%)           5 (  0.9)    5 (  1.1)   0 (  0.0)   0.813     
##   steroid = 1 (%)          2 (  0.4)    1 (  0.2)   1 (  1.4)   0.638     
##   beta = 1 (%)            18 (  3.4)   18 (  3.9)   0 (  0.0)   0.173     
##   vaso = 1 (%)            31 (  5.8)   11 (  2.4)  20 ( 27.4)  <0.001     
##   ffp = 1 (%)            137 ( 25.6)  100 ( 21.6)  37 ( 50.7)  <0.001     
##   pc = 1 (%)               6 (  1.1)    3 (  0.6)   3 (  4.1)   0.044     
##   albner = 1 (%)          47 (  8.8)   32 (  6.9)  15 ( 20.5)  <0.001     
##   shock = 1 (%)          218 ( 40.7)  156 ( 33.7)  62 ( 84.9)  <0.001     
##   hosp_mortality = 1 (%)  73 ( 13.6)    0 (  0.0)  73 (100.0)  <0.001     
##   age_cate = 1 (%)       303 ( 56.5)  259 ( 55.9)  44 ( 60.3)   0.570     
##   bmi_cate = 1 (%)       111 ( 20.7)  101 ( 21.8)  10 ( 13.7)   0.151     
##   gcs_cate = 1 (%)        32 (  6.0)   11 (  2.4)  21 ( 28.8)  <0.001     
##   cci_cate = 1 (%)        78 ( 14.6)   65 ( 14.0)  13 ( 17.8)   0.503     
##   bt_cate = 1 (%)        147 ( 27.4)  131 ( 28.3)  16 ( 21.9)   0.320     
##   sBP_cate = 1 (%)       134 ( 25.0)   78 ( 16.8)  56 ( 76.7)  <0.001     
##   dBP_cate = 1 (%)       228 ( 42.5)  166 ( 35.9)  62 ( 84.9)  <0.001     
##   hr_cate = 1 (%)        121 ( 22.6)   81 ( 17.5)  40 ( 54.8)  <0.001     
##   bil_cate = 1 (%)        40 (  7.5)   23 (  5.0)  17 ( 23.3)  <0.001     
##   ast_cate = 1 (%)        40 (  7.5)   26 (  5.6)  14 ( 19.2)  <0.001     
##   alt_cate = 1 (%)        36 (  6.7)   26 (  5.6)  10 ( 13.7)   0.021     
##   wbc_cate = 1 (%)        89 ( 16.6)   73 ( 15.8)  16 ( 21.9)   0.253     
##   hb_cate = 1 (%)        230 ( 42.9)  193 ( 41.7)  37 ( 50.7)   0.188     
##   plt_cate = 1 (%)       274 ( 51.1)  236 ( 51.0)  38 ( 52.1)   0.963     
##   tp_cate = 1 (%)        211 ( 39.4)  175 ( 37.8)  36 ( 49.3)   0.081     
##   alb_cate = 1 (%)       233 ( 43.5)  175 ( 37.8)  58 ( 79.5)  <0.001     
##   cre_cate = 1 (%)        62 ( 11.6)   37 (  8.0)  25 ( 34.2)  <0.001     
##   crp_cate = 1 (%)        47 (  8.8)   30 (  6.5)  17 ( 23.3)  <0.001     
##   pt_cate = 1 (%)        247 ( 46.1)  200 ( 43.2)  47 ( 64.4)   0.001     
##   aptt_cate = 1 (%)       22 (  4.1)    9 (  1.9)  13 ( 17.8)  <0.001     
##                         Stratified by hosp_mortality
##                          SMD    Missing
##   n                                    
##   sex = F (%)             0.078 0.0    
##   barthel (%)             1.040 0.0    
##      0                                 
##      1                                 
##      2                                 
##   child_score (%)         1.018 0.0    
##      0                                 
##      1                                 
##      2                                 
##   pad = 1 (%)             0.066 0.0    
##   stroke = 1 (%)          0.210 0.0    
##   dimentia = 1 (%)        0.132 0.0    
##   ch_lung = 1 (%)         0.053 0.0    
##   rheumati = 1 (%)        0.114 0.0    
##   pept_ulcer = 1 (%)      0.450 0.0    
##   dm = 1 (%)              0.281 0.0    
##   dm_compli = 1 (%)       0.162 0.0    
##   paralysis = 0 (%)      <0.001 0.0    
##   malignancy = 1 (%)      0.193 0.0    
##   meta_tumor = 1 (%)      0.301 0.0    
##   aids = 0 (%)           <0.001 0.0    
##   eGFR30 = 1 (%)          0.454 0.0    
##   hd = 1 (%)              0.114 0.0    
##   hcc = 1 (%)             0.034 0.0    
##   alcohol = 1 (%)         0.178 0.0    
##   past_rupture = 1 (%)    0.270 0.0    
##   antiplate = 1 (%)       0.072 0.0    
##   anticoag = 1 (%)        0.066 0.0    
##   antithro = 1 (%)        0.072 0.0    
##   nsaids = 1 (%)          0.148 0.0    
##   steroid = 1 (%)         0.130 0.0    
##   beta = 1 (%)            0.284 0.0    
##   vaso = 1 (%)            0.751 0.0    
##   ffp = 1 (%)             0.635 0.0    
##   pc = 1 (%)              0.229 0.0    
##   albner = 1 (%)          0.404 0.0    
##   shock = 1 (%)           1.222 0.0    
##   hosp_mortality = 1 (%)    NaN 0.0    
##   age_cate = 1 (%)        0.088 0.0    
##   bmi_cate = 1 (%)        0.214 0.0    
##   gcs_cate = 1 (%)        0.781 0.0    
##   cci_cate = 1 (%)        0.103 0.0    
##   bt_cate = 1 (%)         0.147 0.0    
##   sBP_cate = 1 (%)        1.500 0.0    
##   dBP_cate = 1 (%)        1.160 0.0    
##   hr_cate = 1 (%)         0.842 0.0    
##   bil_cate = 1 (%)        0.545 0.0    
##   ast_cate = 1 (%)        0.421 0.0    
##   alt_cate = 1 (%)        0.276 0.0    
##   wbc_cate = 1 (%)        0.158 0.0    
##   hb_cate = 1 (%)         0.181 0.0    
##   plt_cate = 1 (%)        0.022 0.0    
##   tp_cate = 1 (%)         0.234 0.0    
##   alb_cate = 1 (%)        0.933 0.0    
##   cre_cate = 1 (%)        0.679 0.0    
##   crp_cate = 1 (%)        0.486 0.0    
##   pt_cate = 1 (%)         0.435 0.0    
##   aptt_cate = 1 (%)       0.552 0.0

tbl_summary

# specify your data and variables
tbl_summary(data = dev_imp, 
            by = "hosp_mortality",
            type = list(gcs ~ "continuous", year ~ "categorical"),
            statistic = all_continuous() ~ "{median} ({p25}, {p75})",
            digits = all_continuous() ~ c(0, 2))

Characteristic	0, N = 4631	1, N = 731
sex
M	352 (76%)	53 (73%)
F	111 (24%)	20 (27%)
barthel
0	186 (40%)	7 (9.6%)
1	142 (31%)	12 (16%)
2	135 (29%)	54 (74%)
child_score
0	65 (14%)	1 (1.4%)
1	271 (59%)	20 (27%)
2	127 (27%)	52 (71%)
pad
0	462 (100%)	73 (100%)
1	1 (0.2%)	0 (0%)
stroke
0	453 (98%)	73 (100%)
1	10 (2.2%)	0 (0%)
dimentia
0	459 (99%)	73 (100%)
1	4 (0.9%)	0 (0%)
ch_lung
0	454 (98%)	71 (97%)
1	9 (1.9%)	2 (2.7%)
rheumati
0	460 (99%)	73 (100%)
1	3 (0.6%)	0 (0%)
pept_ulcer
0	405 (87%)	72 (99%)
1	58 (13%)	1 (1.4%)
dm
0	365 (79%)	65 (89%)
1	98 (21%)	8 (11%)
dm_compli
0	457 (99%)	73 (100%)
1	6 (1.3%)	0 (0%)
paralysis
0	463 (100%)	73 (100%)
malignancy
0	417 (90%)	61 (84%)
1	46 (9.9%)	12 (16%)
meta_tumor
0	461 (100%)	69 (95%)
1	2 (0.4%)	4 (5.5%)
aids
0	463 (100%)	73 (100%)
eGFR30
0	429 (93%)	56 (77%)
1	34 (7.3%)	17 (23%)
hd
0	460 (99%)	73 (100%)
1	3 (0.6%)	0 (0%)
hcc
0	368 (79%)	57 (78%)
1	95 (21%)	16 (22%)
alcohol
0	258 (56%)	47 (64%)
1	205 (44%)	26 (36%)
past_rupture
0	367 (79%)	65 (89%)
1	96 (21%)	8 (11%)
antiplate
0	460 (99%)	72 (99%)
1	3 (0.6%)	1 (1.4%)
anticoag
0	462 (100%)	73 (100%)
1	1 (0.2%)	0 (0%)
antithro
0	460 (99%)	72 (99%)
1	3 (0.6%)	1 (1.4%)
nsaids
0	458 (99%)	73 (100%)
1	5 (1.1%)	0 (0%)
steroid
0	462 (100%)	72 (99%)
1	1 (0.2%)	1 (1.4%)
beta
0	445 (96%)	73 (100%)
1	18 (3.9%)	0 (0%)
vaso
0	452 (98%)	53 (73%)
1	11 (2.4%)	20 (27%)
ffp
0	363 (78%)	36 (49%)
1	100 (22%)	37 (51%)
pc
0	460 (99%)	70 (96%)
1	3 (0.6%)	3 (4.1%)
albner
0	431 (93%)	58 (79%)
1	32 (6.9%)	15 (21%)
shock
0	307 (66%)	11 (15%)
1	156 (34%)	62 (85%)
age	62 (51.00, 70)	66 (55.00, 74)
bmi	23 (20.81, 25)	23 (20.96, 24)
smoke	200 (0.00, 413)	180 (0.00, 400)
child_num	8 (7.00, 10)	11 (9.00, 12)
gcs	15 (15.00, 15)	15 (9.00, 15)
cci_num	4 (4.00, 5)	4 (4.00, 5)
map	4 (0.00, 4)	6 (4.00, 8)
bt	37 (36.30, 37)	36 (36.00, 37)
sBP	90 (82.00, 98)	65 (59.00, 79)
dBP	53 (46.00, 59)	40 (33.00, 46)
hr	80 (70.00, 93)	101 (89.00, 112)
bil	1 (0.90, 2)	3 (1.66, 5)
ast	55 (33.00, 90)	84 (49.00, 153)
alt	29 (20.00, 44)	33 (25.00, 63)
wbc	7,200 (5,315.00, 10,240)	7,990 (6,200.00, 10,800)
hb	8 (7.00, 10)	8 (6.60, 10)
plt	98 (73.00, 135)	98 (76.00, 130)
tp	6 (5.60, 7)	6 (5.20, 6)
alb	3 (2.60, 3)	2 (1.80, 3)
eGFR	71 (52.42, 92)	44 (30.40, 58)
bun	23 (15.50, 35)	28 (18.10, 46)
cre	1 (0.66, 1)	1 (0.99, 2)
crp	0 (0.11, 1)	1 (0.40, 2)
pt	54 (43.00, 64)	40 (26.80, 55)
aptt	28 (26.05, 32)	32 (29.10, 44)
los	10 (7.00, 18)	4 (1.00, 16)
meld	10 (7.00, 14)	16 (13.00, 25)
pt_id	359 (149.50, 583)	426 (235.00, 599)
hosp_num
1	404 (87%)	68 (93%)
2	43 (9.3%)	5 (6.8%)
3	15 (3.2%)	0 (0%)
4	1 (0.2%)	0 (0%)
hosp_id	1,010 (1,003.00, 1,024)	1,017 (1,005.00, 1,024)
year
2010	68 (15%)	8 (11%)
2011	70 (15%)	9 (12%)
2012	72 (16%)	16 (22%)
2013	55 (12%)	15 (21%)
2014	69 (15%)	8 (11%)
2015	67 (14%)	5 (6.8%)
2016	62 (13%)	12 (16%)
age_cate	259 (56%)	44 (60%)
bmi_cate	101 (22%)	10 (14%)
gcs_cate	11 (2.4%)	21 (29%)
cci_cate	65 (14%)	13 (18%)
bt_cate	131 (28%)	16 (22%)
sBP_cate	78 (17%)	56 (77%)
dBP_cate	166 (36%)	62 (85%)
hr_cate	81 (17%)	40 (55%)
bil_cate	23 (5.0%)	17 (23%)
ast_cate	26 (5.6%)	14 (19%)
alt_cate	26 (5.6%)	10 (14%)
wbc_cate	73 (16%)	16 (22%)
hb_cate	193 (42%)	37 (51%)
plt_cate	236 (51%)	38 (52%)
tp_cate	175 (38%)	36 (49%)
alb_cate	175 (38%)	58 (79%)
cre_cate	37 (8.0%)	25 (34%)
crp_cate	30 (6.5%)	17 (23%)
pt_cate	200 (43%)	47 (64%)
aptt_cate	9 (1.9%)	13 (18%)
1 n (%); Median (IQR)

カテゴリー変換後の変数のclass確認

# "initial_vars" vector containing the names of variables
initial_vars <- c("sex", "barthel", "pept_ulcer", "dm", "malignancy", "hcc", "alcohol", 
                  "past_rupture", "antithro", "steroid", "beta", "sBP_cate", 
                  "age_cate", "bmi_cate", "gcs_cate", "bt_cate", "bil_cate", "ast_cate", "alt_cate", 
                  "wbc_cate", "hb_cate", "plt_cate", "alb_cate", "cre_cate", 
                  "crp_cate", "pt_cate","aptt_cate")

# Loop through each variable in "initial_vars" and print its class
for (var in initial_vars) {
  print(paste(var, ": ", class(dev_imp[[var]]), sep=""))
}

## [1] "sex: factor"
## [1] "barthel: factor"
## [1] "pept_ulcer: factor"
## [1] "dm: factor"
## [1] "malignancy: factor"
## [1] "hcc: factor"
## [1] "alcohol: factor"
## [1] "past_rupture: factor"
## [1] "antithro: factor"
## [1] "steroid: factor"
## [1] "beta: factor"
## [1] "sBP_cate: numeric"
## [1] "age_cate: numeric"
## [1] "bmi_cate: numeric"
## [1] "gcs_cate: numeric"
## [1] "bt_cate: numeric"
## [1] "bil_cate: numeric"
## [1] "ast_cate: numeric"
## [1] "alt_cate: numeric"
## [1] "wbc_cate: numeric"
## [1] "hb_cate: numeric"
## [1] "plt_cate: numeric"
## [1] "alb_cate: numeric"
## [1] "cre_cate: numeric"
## [1] "crp_cate: numeric"
## [1] "pt_cate: numeric"
## [1] "aptt_cate: numeric"

初期変数(臨床的観点から)とLassoでの変数選択

# 変数リストの設定（応答変数を除外）
initial_vars <- c("sex", "barthel", "pept_ulcer", "dm", "malignancy", "hcc", "alcohol", 
                  "past_rupture", "antithro", "steroid", "beta", "sBP_cate", 
                  "age_cate", "bmi_cate", "gcs_cate", "bt_cate", "bil_cate", "ast_cate", "alt_cate", 
                  "wbc_cate", "hb_cate", "plt_cate", "alb_cate", "cre_cate", 
                  "crp_cate", "pt_cate","aptt_cate")

# one-hot encoding を行う
x <- model.matrix(~.-1, dev_imp[initial_vars])

# y のデータ型変換
y <- as.numeric(as.character(dev_imp$hosp_mortality))

# 乱数のシードを設定
set.seed(2023)

# cv.glmnetを用いた1SE ruleによるLasso
cvfit <- cv.glmnet(x, y, family = "binomial")


# lambda.1seを用いての最適モデルの選択
lasso_model <- glmnet(x, y, family = "binomial", alpha = 1, lambda = cvfit$lambda.1se)

# 変数の重要度の表示
coef(lasso_model)

## 30 x 1 sparse Matrix of class "dgCMatrix"
##                       s0
## (Intercept)   -3.1080828
## sexM           .        
## sexF           .        
## barthel1       .        
## barthel2       0.3591292
## pept_ulcer1    .        
## dm1            .        
## malignancy1    .        
## hcc1           .        
## alcohol1       .        
## past_rupture1  .        
## antithro1      .        
## steroid1       .        
## beta1          .        
## sBP_cate       1.6587327
## age_cate       .        
## bmi_cate       .        
## gcs_cate       0.9321486
## bt_cate        .        
## bil_cate       0.5057012
## ast_cate       0.3036268
## alt_cate       .        
## wbc_cate       .        
## hb_cate        .        
## plt_cate       .        
## alb_cate       0.3194390
## cre_cate       0.5613715
## crp_cate       .        
## pt_cate        .        
## aptt_cate      .

Goodness of fitを計算する

# hosp_mortalityを数値に変換
dev_imp$hosp_mortality <- as.numeric(as.character(dev_imp$hosp_mortality))

# 予測変数として選択された変数を使って新たにモデルを作成
fit_reduced_model <- lrm(hosp_mortality ~ sBP_cate +gcs_cate + bil_cate + cre_cate + alb_cate + ast_cate,  data = dev_imp)

# 予測確率を計算
dev_imp$fitted <- predict(fit_reduced_model, type="fitted")

# Calibration in large
calibration <- mean(dev_imp$hosp_mortality) - mean(dev_imp$fitted)
fitstat <- data.frame(Name = "Calibration in the large", Value = calibration)

# Brier Score
dev_imp$diff2 <- (dev_imp$fitted - dev_imp$hosp_mortality)^2
brier_score <- mean(dev_imp$diff2)
fitstat <- rbind(fitstat, data.frame(Name = "Brier score", Value = brier_score))

# Pseudo R-square
pseudo_r2 <- fit_reduced_model$stats["R2"]
fitstat <- rbind(fitstat, data.frame(Name = "R2", Value = pseudo_r2))

# AIC
aic <- AIC(fit_reduced_model)
fitstat <- rbind(fitstat, data.frame(Name = "AIC", Value = aic))

# Change the display option
options(scipen = 999)

# Display the final table
fitstat

##                        Name                   Value
## 1  Calibration in the large  -0.0000000000000121847
## 2               Brier score   0.0719239166244594191
## R2                       R2   0.4905109701691195978
## 11                      AIC 272.5339099257948873856

ROCを記載

# Load necessary libraries
library(pROC)
library(ggplot2)

# Re-run the model using the selected variables from earlier
fit_reduced_model <- lrm(hosp_mortality ~ sBP_cate +gcs_cate + bil_cate + cre_cate + alb_cate+ ast_cate,  data = dev_imp)

# Calculate fitted probabilities
dev_imp$fitted <- predict(fit_reduced_model, type="fitted")

# Create ROC object, setting the response variable and predictor
roc_obj <- roc(dev_imp$hosp_mortality ~ dev_imp$fitted, ci=TRUE, direction="auto")

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

# Plot ROC curve
roc_plot <- ggroc(roc_obj)

# Add the theme you used before. For example, if you used theme_minimal:
roc_plot <- roc_plot + theme_minimal()


# Calculate AUC and its confidence interval
auc_roc <- auc(roc_obj)
ci <- ci.auc(roc_obj)

# Calculate the upper bound of the CI
upper_bound <- 2*auc_roc - ci[1]

# Print the AUC and its confidence interval
cat("AUC for the development logistic model: ", auc_roc, "\n")

## AUC for the development logistic model:  0.9008107

cat("95% CI for AUC: (", ci[1], ",", upper_bound, ")\n")

## 95% CI for AUC: ( 0.8624818 , 0.9391396 )

# Load necessary libraries
library(pROC)
library(ggplot2)

# Re-run the model using the selected variables from earlier
fit_reduced_model <- lrm(hosp_mortality ~ sBP_cate +gcs_cate + bil_cate + cre_cate + alb_cate+ ast_cate,  data = dev_imp)

# Calculate fitted probabilities
dev_imp$fitted <- predict(fit_reduced_model, type="fitted")

# Create ROC object, setting the response variable and predictor
roc_obj <- roc(dev_imp$hosp_mortality ~ dev_imp$fitted, ci=TRUE, direction="auto")

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

# Plot ROC curve
plot(roc_obj, main="ROC curve for the development logistic model")

cat("AUC for the development logistic mode: ", auc(roc_obj), "\n")

## AUC for the development logistic mode:  0.9008107

calibration plotの作成

# Create calibration plot
val.prob(dev_imp$fitted, dev_imp$hosp_mortality, g=10, cex=.5)

##                     Dxy                 C (ROC)                      R2 
##   0.8016213497440752844   0.9008106748720375867   0.4905109701691195978 
##                       D                D:Chi-sq                     D:p 
##   0.3117827989749756834 168.1155802505869587549                      NA 
##                       U                U:Chi-sq                     U:p 
##  -0.0037313432835811349   0.0000000000005115908   0.9999999999997442046 
##                       Q                   Brier               Intercept 
##   0.3155141422585568400   0.0719239166244594191  -0.0000000308643282759 
##                   Slope                    Emax                     E90 
##   0.9999999099259625490   0.0351614493164458461   0.0271257964343915581 
##                    Eavg                     S:z                     S:p 
##   0.0095307861857258959   0.1322811866957885052   0.8947618874430839364

内的検証　ブードストラップ　B=1000

# Re-run the model using the selected variables from earlier
fit_reduced_model <- lrm(hosp_mortality ~ sBP_cate +gcs_cate + bil_cate + cre_cate + alb_cate+ ast_cate,  data = dev_imp, x = TRUE, y = TRUE)

# RMSの関数を使用してブートストラップによる内的検証を実行
cv <- validate(fit_reduced_model, bw = FALSE, B = 1000, method = "boot", seed = 2023)

# Corrected AUCの計算
corrected_AUC <- cv[1, 5] * 0.5 + 0.5

# 結果の表示
cv

##           index.orig training    test optimism index.corrected    n
## Dxy           0.8016   0.8044  0.7923   0.0121          0.7895 1000
## R2            0.4905   0.5039  0.4777   0.0262          0.4643 1000
## Intercept     0.0000   0.0000 -0.0463   0.0463         -0.0463 1000
## Slope         1.0000   1.0000  0.9458   0.0542          0.9458 1000
## Emax          0.0000   0.0000  0.0204   0.0204          0.0204 1000
## D             0.3118   0.3218  0.3022   0.0196          0.2922 1000
## U            -0.0037  -0.0037  0.0010  -0.0048          0.0010 1000
## Q             0.3155   0.3255  0.3012   0.0243          0.2912 1000
## B             0.0719   0.0697  0.0740  -0.0043          0.0763 1000
## g             1.8355   1.9110  1.7890   0.1221          1.7135 1000
## gp            0.1862   0.1875  0.1838   0.0036          0.1826 1000

print("corrected_AUC")

## [1] "corrected_AUC"

print(corrected_AUC)

## [1] 0.8947695

信頼区間も出す

# 必要なパッケージの読み込み
library(pROC)

# 各ブートストラップサンプルでAUCを保存するためのベクトルの初期化
aucs <- numeric(1000)

# ブートストラップ検証
set.seed(2023)  # 再現性のための固定シード
for(i in 1:1000) {
  # ブートストラップサンプルの作成
  sample_rows <- sample(nrow(dev_imp), replace = TRUE)
  boot_data <- dev_imp[sample_rows, ]
  
  # モデルのフィット
  fit <- lrm(hosp_mortality ~ sBP_cate + gcs_cate + bil_cate + cre_cate + alb_cate+ ast_cate,  data = boot_data)
  
  # 予測値の計算とAUCの保存
  predictions <- predict(fit, type = "fitted")
  aucs[i] <- auc(roc(response = boot_data$hosp_mortality, predictor = predictions))
}

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

# 信頼区間の計算と表示
ci <- quantile(aucs, c(0.025, 0.975))
cat("Bootstrap 95% CI for AUC: (", ci[1], ",", ci[2], ")\n")

## Bootstrap 95% CI for AUC: ( 0.8629952 , 0.9391768 )

内的検証　AUC以外　ブードストラップ　B=1000

# Load necessary library
library(boot)

## 
##  次のパッケージを付け加えます: 'boot'

##  以下のオブジェクトは 'package:car' からマスクされています:
## 
##     logit

# Define a function to calculate the metrics
calc_metrics <- function(data, indices) {
  data_boot <- data[indices, ]
  fit_model <- lrm(hosp_mortality ~ sBP_cate +gcs_cate + bil_cate + cre_cate + alb_cate+ ast_cate, data = data_boot, x = TRUE, y = TRUE)
  
  predicted <- predict(fit_model, type="fitted")
  
  calibration <- mean(data_boot$hosp_mortality) - mean(predicted)
  brier_score <- mean((predicted - data_boot$hosp_mortality)^2)
  pseudo_r2 <- fit_model$stats["R2"]
  aic <- AIC(fit_model)
  
  return(c(calibration, brier_score, pseudo_r2, aic))
}

# Bootstrap resampling
set.seed(2023)
results <- boot(data = dev_imp, statistic = calc_metrics, R = 1000)

# Display the results
print(results)

## 
## ORDINARY NONPARAMETRIC BOOTSTRAP
## 
## 
## Call:
## boot(data = dev_imp, statistic = calc_metrics, R = 1000)
## 
## 
## Bootstrap Statistics :
##                    original              bias            std. error
## t1*  -0.0000000000000121847 -0.0000000002605864  0.0000000008744432
## t2*   0.0719239166244594191 -0.0022871601766219  0.0076839672154270
## t3*   0.4905109701691195978  0.0139845383018452  0.0523076756937815
## t4* 272.5339099257948873856 -7.5531821155879015 25.7247642845896607

検証データに対するmissforestによる単一代入を含めた外的検証の準備

###準備

# vari_cat.f (defined as above) in the data_original is changed to factor type and set into data_factor_for_imp
data_factor_for_imp_val <- as.data.frame(lapply(df_val[vari_cat.f],as.factor))
#check all the variable is factor
str(data_factor_for_imp_val)

## 'data.frame':    444 obs. of  32 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 2 1 1 1 2 1 2 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": NA 3 2 1 3 1 2 1 1 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 2 2 2 2 1 2 2 2 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 1 2 1 2 1 1 2 2 2 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 2 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 2 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 1 1 1 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 1 NA 1 1 1 1 1 1 1 1 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...

#summary numeric type
data_numeric_for_imp_val <- as.data.frame(lapply(df_val[vari_numeric],as.numeric))
# check all the variable is numeric
str(data_numeric_for_imp_val)

## 'data.frame':    444 obs. of  27 variables:
##  $ age      : num  47 62 82 57 69 47 50 68 75 53 ...
##  $ bmi      : num  21 20.8 26.9 23.2 23.6 ...
##  $ smoke    : num  270 0 0 0 0 135 NA 0 0 0 ...
##  $ child_num: num  11 9 8 8 8 6 7 7 8 14 ...
##  $ gcs      : num  15 13 15 15 15 15 15 15 15 3 ...
##  $ cci_num  : num  4 4 4 3 6 5 4 4 5 5 ...
##  $ map      : num  0 8 6 2 4 0 2 4 4 4 ...
##  $ bt       : num  36.4 NA 36.9 37.1 37.2 36.1 36.5 37 37 36 ...
##  $ sBP      : num  102 NA 95 111 125 116 102 96 97 71 ...
##  $ dBP      : num  67 NA 49 68 70 84 88 55 54 49 ...
##  $ hr       : num  83 NA 60 101 121 56 79 72 75 56 ...
##  $ bil      : num  7.5 0.5 0.91 2.35 2.49 0.76 1.84 2.18 1.13 3.88 ...
##  $ ast      : num  112 56 37 42 74 18 19 24 31 62 ...
##  $ alt      : num  53 30 19 35 43 13 18 16 25 44 ...
##  $ wbc      : num  9800 14600 3350 12030 6800 ...
##  $ hb       : num  12.2 3.7 7.7 9.8 11.2 10.7 6.6 12.9 10.4 7.7 ...
##  $ plt      : num  69 437 109 107 107 60 140 84 152 178 ...
##  $ tp       : num  5.7 5.6 5.8 5.4 5.9 NA 5.9 6.1 7 6.8 ...
##  $ alb      : num  2.6 2.1 2.8 3.4 3 3.7 3.4 3.7 3.3 2.7 ...
##  $ eGFR     : num  89.3 65.8 46.6 94.2 90.7 ...
##  $ bun      : num  13.7 42.8 24.2 26.9 7.9 7.6 35 24.9 35.3 23.9 ...
##  $ cre      : num  0.74 0.69 1.16 0.67 0.66 0.54 0.97 0.59 0.66 1.43 ...
##  $ crp      : num  0.1 0.17 0.22 0.04 0.111 ...
##  $ pt       : num  28.5 51.3 60.4 54.8 54 80 70 68 78 69 ...
##  $ aptt     : num  37.8 30.9 30.2 33.4 34.5 NA 29.1 30.2 33.1 30.2 ...
##  $ los      : num  6 2 14 5 12 4 1 6 9 16 ...
##  $ meld     : num  20 6 10 9 10 6 10 7 6 17 ...

# combine the factor type and numeric type
data_for_imp_val <- cbind(data_factor_for_imp_val, data_numeric_for_imp_val) 
#check all the variable type
str(data_for_imp_val)

## 'data.frame':    444 obs. of  59 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 2 1 1 1 2 1 2 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": NA 3 2 1 3 1 2 1 1 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 2 2 2 2 1 2 2 2 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 1 2 1 2 1 1 2 2 2 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 2 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 2 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 1 1 1 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 1 NA 1 1 1 1 1 1 1 1 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ age           : num  47 62 82 57 69 47 50 68 75 53 ...
##  $ bmi           : num  21 20.8 26.9 23.2 23.6 ...
##  $ smoke         : num  270 0 0 0 0 135 NA 0 0 0 ...
##  $ child_num     : num  11 9 8 8 8 6 7 7 8 14 ...
##  $ gcs           : num  15 13 15 15 15 15 15 15 15 3 ...
##  $ cci_num       : num  4 4 4 3 6 5 4 4 5 5 ...
##  $ map           : num  0 8 6 2 4 0 2 4 4 4 ...
##  $ bt            : num  36.4 NA 36.9 37.1 37.2 36.1 36.5 37 37 36 ...
##  $ sBP           : num  102 NA 95 111 125 116 102 96 97 71 ...
##  $ dBP           : num  67 NA 49 68 70 84 88 55 54 49 ...
##  $ hr            : num  83 NA 60 101 121 56 79 72 75 56 ...
##  $ bil           : num  7.5 0.5 0.91 2.35 2.49 0.76 1.84 2.18 1.13 3.88 ...
##  $ ast           : num  112 56 37 42 74 18 19 24 31 62 ...
##  $ alt           : num  53 30 19 35 43 13 18 16 25 44 ...
##  $ wbc           : num  9800 14600 3350 12030 6800 ...
##  $ hb            : num  12.2 3.7 7.7 9.8 11.2 10.7 6.6 12.9 10.4 7.7 ...
##  $ plt           : num  69 437 109 107 107 60 140 84 152 178 ...
##  $ tp            : num  5.7 5.6 5.8 5.4 5.9 NA 5.9 6.1 7 6.8 ...
##  $ alb           : num  2.6 2.1 2.8 3.4 3 3.7 3.4 3.7 3.3 2.7 ...
##  $ eGFR          : num  89.3 65.8 46.6 94.2 90.7 ...
##  $ bun           : num  13.7 42.8 24.2 26.9 7.9 7.6 35 24.9 35.3 23.9 ...
##  $ cre           : num  0.74 0.69 1.16 0.67 0.66 0.54 0.97 0.59 0.66 1.43 ...
##  $ crp           : num  0.1 0.17 0.22 0.04 0.111 ...
##  $ pt            : num  28.5 51.3 60.4 54.8 54 80 70 68 78 69 ...
##  $ aptt          : num  37.8 30.9 30.2 33.4 34.5 NA 29.1 30.2 33.1 30.2 ...
##  $ los           : num  6 2 14 5 12 4 1 6 9 16 ...
##  $ meld          : num  20 6 10 9 10 6 10 7 6 17 ...

###実行

cores <- detectCores(logical = FALSE) ###並列化処理
registerDoParallel(cores = cores) ###並列化処理
set.seed(2023)
md.pattern(data_for_imp_val) #see patern the missing

##     sex pad stroke dimentia ch_lung rheumati pept_ulcer dm dm_compli paralysis
## 269   1   1      1        1       1        1          1  1         1         1
## 13    1   1      1        1       1        1          1  1         1         1
## 9     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 14    1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 20    1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 7     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 7     1   1      1        1       1        1          1  1         1         1
## 8     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 13    1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 12    1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 17    1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 8     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 5     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 6     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 2     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
## 1     1   1      1        1       1        1          1  1         1         1
##       0   0      0        0       0        0          0  0         0         0
##     malignancy meta_tumor aids hd hcc alcohol past_rupture antiplate anticoag
## 269          1          1    1  1   1       1            1         1        1
## 13           1          1    1  1   1       1            1         1        1
## 9            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 14           1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 20           1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 7            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 7            1          1    1  1   1       1            1         1        1
## 8            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 13           1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 12           1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 17           1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 8            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 5            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 6            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 2            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
## 1            1          1    1  1   1       1            1         1        1
##              0          0    0  0   0       0            0         0        0
##     antithro nsaids steroid beta vaso ffp pc albner hosp_mortality age gcs
## 269        1      1       1    1    1   1  1      1              1   1   1
## 13         1      1       1    1    1   1  1      1              1   1   1
## 9          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 14         1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 20         1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 7          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 7          1      1       1    1    1   1  1      1              1   1   1
## 8          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 13         1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 12         1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 17         1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 8          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 5          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 6          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 2          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
## 1          1      1       1    1    1   1  1      1              1   1   1
##            0      0       0    0    0   0  0      0              0   0   0
##     cci_num map los sBP dBP shock hr bt eGFR30 ast alt wbc hb plt eGFR bun bil
## 269       1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 13        1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 9         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 14        1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 20        1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 7         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 7         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 8         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 13        1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 12        1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 17        1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 2         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 8         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 2         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 5         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 2         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 2         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   0
## 1         1   1   1   1   1     1  1  1      1   1   1   1  1   1    1   1   0
## 6         1   1   1   1   1     1  1  1      0   0   0   0  0   0    0   0   0
## 1         1   1   1   1   1     1  1  1      0   0   0   0  0   0    0   0   0
## 2         1   1   1   1   1     1  1  0      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     0  0  1      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     0  0  0      1   1   1   1  1   1    1   1   1
## 1         1   1   1   1   1     0  0  0      1   1   1   1  1   1    1   1   1
## 1         1   1   1   0   0     0  0  0      1   1   1   1  1   1    1   1   1
##           0   0   0   1   1     4  4  5      7   7   7   7  7   7    7   7   9
##     cre alb crp pt barthel meld bmi tp child_score smoke child_num aptt    
## 269   1   1   1  1       1    1   1  1           1     1         1    1   0
## 13    1   1   1  1       1    1   1  1           1     1         1    0   1
## 9     1   1   1  1       1    1   1  1           1     1         0    1   1
## 1     1   1   1  1       1    1   1  1           1     1         0    0   2
## 14    1   1   1  1       1    1   1  1           1     0         1    1   1
## 1     1   1   1  1       1    1   1  1           1     0         1    0   2
## 1     1   1   1  1       1    1   1  1           1     0         0    1   2
## 20    1   1   1  1       1    1   1  1           0     1         0    1   2
## 1     1   1   1  1       1    1   1  1           0     1         0    0   3
## 7     1   1   1  1       1    1   1  1           0     0         0    1   3
## 1     1   1   1  1       1    1   1  1           0     0         0    0   4
## 7     1   1   1  1       1    1   1  0           1     1         1    1   1
## 8     1   1   1  1       1    1   1  0           1     1         1    0   2
## 1     1   1   1  1       1    1   1  0           1     1         0    1   2
## 13    1   1   1  1       1    1   0  1           1     1         1    1   1
## 1     1   1   1  1       1    1   0  1           1     1         1    0   2
## 12    1   1   1  1       1    1   0  1           1     0         1    1   2
## 1     1   1   1  1       1    1   0  0           1     1         1    0   3
## 1     1   1   1  1       1    0   1  1           1     1         1    1   1
## 17    1   1   1  1       0    1   1  1           1     1         1    1   1
## 2     1   1   1  1       0    1   1  1           1     1         1    0   2
## 1     1   1   1  1       0    1   1  1           1     0         1    1   2
## 1     1   1   1  1       0    1   1  1           1     0         0    1   3
## 1     1   1   1  1       0    1   1  0           1     1         1    1   2
## 1     1   1   1  1       0    1   0  1           1     1         1    1   2
## 8     1   1   1  0       1    0   1  1           1     1         1    0   3
## 2     1   1   1  0       1    0   1  1           0     1         0    0   5
## 1     1   1   1  0       1    0   1  1           0     0         0    0   6
## 1     1   1   1  0       1    0   1  0           1     1         1    0   4
## 1     1   1   1  0       1    0   1  0           1     0         1    0   5
## 5     1   1   0  1       1    1   1  1           1     1         1    1   1
## 1     1   0   1  1       1    1   1  1           0     1         0    1   3
## 2     1   0   1  1       1    1   1  0           1     1         1    1   2
## 1     1   0   0  1       1    1   1  0           1     1         1    1   3
## 2     0   1   1  1       1    0   1  1           1     1         1    1   2
## 1     0   1   0  1       1    0   1  1           0     1         0    1   5
## 1     1   1   0  1       1    0   1  1           1     0         1    1   4
## 1     1   0   1  1       1    0   1  0           1     1         1    1   4
## 6     0   0   0  0       1    0   1  0           1     1         1    0  16
## 1     0   0   0  0       1    0   1  0           0     1         0    0  18
## 2     1   1   1  1       1    1   0  1           1     1         1    1   2
## 1     1   1   1  1       0    1   1  1           1     1         1    1   3
## 1     1   1   1  1       1    1   1  1           1     0         1    1   4
## 1     1   1   1  1       1    1   1  0           1     1         1    1   4
## 1     1   1   1  1       1    1   1  1           1     1         1    1   5
##      10  12  15 20      24   26  30 32          35    42        48   49 423

imp.mf_val <- missForest(data_for_imp_val,
                     maxiter = 10,
                     ntree = 100,
                     mtry = floor(sqrt(ncol(data_for_imp_val))),
                     parallelize = "variables",
                     verbose = TRUE)

##   parallelizing over the variables of the input data matrix 'xmis'
##   missForest iteration 1 in progress...done!
##     estimated error(s): 0.453138 0.02131973 
##     difference(s): 0.00009726191 0.001618806 
##     time: 0.89 seconds
## 
##   missForest iteration 2 in progress...done!
##     estimated error(s): 0.4419382 0.0192313 
##     difference(s): 0.0001107889 0.0004222973 
##     time: 0.926 seconds
## 
##   missForest iteration 3 in progress...done!
##     estimated error(s): 0.4409547 0.0197921 
##     difference(s): 0.0000704322 0.0005630631 
##     time: 0.932 seconds
## 
##   missForest iteration 4 in progress...done!
##     estimated error(s): 0.4446167 0.02060827 
##     difference(s): 0.00007781678 0.0004222973 
##     time: 0.921 seconds
## 
##   missForest iteration 5 in progress...done!
##     estimated error(s): 0.4480345 0.01957716 
##     difference(s): 0.00005413141 0.0005630631 
##     time: 0.964 seconds
## 
##   missForest iteration 6 in progress...done!
##     estimated error(s): 0.4424111 0.01935843 
##     difference(s): 0.00005504244 0.0003519144 
##     time: 0.932 seconds
## 
##   missForest iteration 7 in progress...done!
##     estimated error(s): 0.44556 0.01935092 
##     difference(s): 0.00008066198 0.0004222973 
##     time: 0.918 seconds

summary(imp.mf_val$ximp)

##  sex     barthel child_score pad     stroke  dimentia ch_lung rheumati
##  M:329   0:174   0: 83       0:443   0:433   0:438    0:440   0:444   
##  F:115   1:141   1:240       1:  1   1: 11   1:  6    1:  4   1:  0   
##          2:129   2:121                                                
##                                                                       
##                                                                       
##                                                                       
##  pept_ulcer dm      dm_compli paralysis malignancy meta_tumor aids    eGFR30 
##  0:402      0:340   0:437     0:444     0:387      0:433      0:444   0:408  
##  1: 42      1:104   1:  7               1: 57      1: 11              1: 36  
##                                                                              
##                                                                              
##                                                                              
##                                                                              
##  hd      hcc     alcohol past_rupture antiplate anticoag antithro nsaids 
##  0:434   0:380   0:203   0:323        0:441     0:441    0:438    0:440  
##  1: 10   1: 64   1:241   1:121        1:  3     1:  3    1:  6    1:  4  
##                                                                          
##                                                                          
##                                                                          
##                                                                          
##  steroid beta    vaso    ffp     pc      albner  shock   hosp_mortality
##  0:441   0:404   0:417   0:290   0:433   0:411   0:248   0:399         
##  1:  3   1: 40   1: 27   1:154   1: 11   1: 33   1:196   1: 45         
##                                                                        
##                                                                        
##                                                                        
##                                                                        
##       age             bmi             smoke          child_num     
##  Min.   :24.00   Min.   : 15.28   Min.   :   0.0   Min.   : 5.000  
##  1st Qu.:50.00   1st Qu.: 20.76   1st Qu.:   0.0   1st Qu.: 7.000  
##  Median :60.00   Median : 23.11   Median :   0.0   Median : 8.000  
##  Mean   :60.42   Mean   : 24.37   Mean   : 228.5   Mean   : 8.426  
##  3rd Qu.:70.00   3rd Qu.: 25.99   3rd Qu.: 320.0   3rd Qu.:10.000  
##  Max.   :93.00   Max.   :339.76   Max.   :2100.0   Max.   :14.000  
##       gcs           cci_num            map               bt       
##  Min.   : 3.00   Min.   : 3.000   Min.   : 0.000   Min.   :33.90  
##  1st Qu.:15.00   1st Qu.: 4.000   1st Qu.: 0.000   1st Qu.:36.50  
##  Median :15.00   Median : 4.000   Median : 4.000   Median :36.80  
##  Mean   :14.61   Mean   : 4.505   Mean   : 3.214   Mean   :36.81  
##  3rd Qu.:15.00   3rd Qu.: 5.000   3rd Qu.: 4.000   3rd Qu.:37.10  
##  Max.   :15.00   Max.   :13.000   Max.   :20.000   Max.   :40.20  
##       sBP              dBP              hr              bil        
##  Min.   : 50.00   Min.   :22.00   Min.   : 41.00   Min.   : 0.200  
##  1st Qu.: 80.00   1st Qu.:46.00   1st Qu.: 73.00   1st Qu.: 1.000  
##  Median : 91.08   Median :54.00   Median : 85.00   Median : 1.660  
##  Mean   : 89.96   Mean   :54.84   Mean   : 88.46   Mean   : 2.415  
##  3rd Qu.:100.00   3rd Qu.:63.00   3rd Qu.:101.00   3rd Qu.: 2.993  
##  Max.   :149.00   Max.   :92.00   Max.   :160.00   Max.   :16.180  
##       ast               alt              wbc              hb        
##  Min.   :  13.00   Min.   :  7.00   Min.   : 2100   Min.   : 2.100  
##  1st Qu.:  32.00   1st Qu.: 19.00   1st Qu.: 5800   1st Qu.: 7.200  
##  Median :  50.00   Median : 29.00   Median : 8215   Median : 8.800  
##  Mean   :  82.03   Mean   : 39.96   Mean   : 9046   Mean   : 8.952  
##  3rd Qu.:  88.25   3rd Qu.: 44.00   3rd Qu.:11070   3rd Qu.:10.500  
##  Max.   :1122.00   Max.   :462.00   Max.   :58400   Max.   :16.500  
##       plt                tp             alb             eGFR        
##  Min.   :  16.00   Min.   :3.000   Min.   :1.400   Min.   :  5.057  
##  1st Qu.:  75.75   1st Qu.:5.600   1st Qu.:2.500   1st Qu.: 49.568  
##  Median : 103.00   Median :6.100   Median :2.900   Median : 67.749  
##  Mean   : 117.56   Mean   :6.149   Mean   :2.911   Mean   : 70.869  
##  3rd Qu.: 140.25   3rd Qu.:6.700   3rd Qu.:3.400   3rd Qu.: 90.404  
##  Max.   :1073.00   Max.   :9.000   Max.   :4.900   Max.   :186.229  
##       bun              cre             crp                pt        
##  Min.   :  3.50   Min.   :0.300   Min.   : 0.0000   Min.   :  9.99  
##  1st Qu.: 16.48   1st Qu.:0.650   1st Qu.: 0.1195   1st Qu.: 46.20  
##  Median : 24.90   Median :0.820   Median : 0.2890   Median : 59.41  
##  Mean   : 28.51   Mean   :1.046   Mean   : 0.8568   Mean   : 59.11  
##  3rd Qu.: 36.40   3rd Qu.:1.110   3rd Qu.: 0.7615   3rd Qu.: 72.00  
##  Max.   :107.80   Max.   :9.080   Max.   :18.9370   Max.   :100.20  
##       aptt             los              meld      
##  Min.   : 18.90   Min.   :  0.00   Min.   : 6.00  
##  1st Qu.: 29.84   1st Qu.:  5.00   1st Qu.: 6.00  
##  Median : 32.40   Median :  7.00   Median :10.00  
##  Mean   : 35.85   Mean   : 10.67   Mean   :11.57  
##  3rd Qu.: 37.18   3rd Qu.: 13.00   3rd Qu.:15.00  
##  Max.   :200.01   Max.   :217.00   Max.   :40.00

md.pattern(imp.mf_val$ximp)

##  /\     /\
## {  `---'  }
## {  O   O  }
## ==>  V <==  No need for mice. This data set is completely observed.
##  \  \|/  /
##   `-----'

##     sex barthel child_score pad stroke dimentia ch_lung rheumati pept_ulcer dm
## 444   1       1           1   1      1        1       1        1          1  1
##       0       0           0   0      0        0       0        0          0  0
##     dm_compli paralysis malignancy meta_tumor aids eGFR30 hd hcc alcohol
## 444         1         1          1          1    1      1  1   1       1
##             0         0          0          0    0      0  0   0       0
##     past_rupture antiplate anticoag antithro nsaids steroid beta vaso ffp pc
## 444            1         1        1        1      1       1    1    1   1  1
##                0         0        0        0      0       0    0    0   0  0
##     albner shock hosp_mortality age bmi smoke child_num gcs cci_num map bt sBP
## 444      1     1              1   1   1     1         1   1       1   1  1   1
##          0     0              0   0   0     0         0   0       0   0  0   0
##     dBP hr bil ast alt wbc hb plt tp alb eGFR bun cre crp pt aptt los meld  
## 444   1  1   1   1   1   1  1   1  1   1    1   1   1   1  1    1   1    1 0
##       0  0   0   0   0   0  0   0  0   0    0   0   0   0  0    0   0    0 0

#if exclude ID is necessary add the ID
data_imp_val <- imp.mf_val$ximp
data_imp_val$pt_id <- df_val$pt_id
data_imp_val$hosp_num<- df_val$hosp_num
data_imp_val$hosp_id<- df_val$hosp_id
data_imp_val$year<- df_val$year
#check
str(data_imp_val)

## 'data.frame':    444 obs. of  63 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 2 1 1 1 2 1 2 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": 2 3 2 1 3 1 2 1 1 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 2 2 2 2 1 2 2 2 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 1 2 1 2 1 1 2 2 2 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 2 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 2 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 1 1 1 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ age           : num  47 62 82 57 69 47 50 68 75 53 ...
##  $ bmi           : num  21 20.8 26.9 23.2 23.6 ...
##  $ smoke         : num  270 0 0 0 0 ...
##  $ child_num     : num  11 9 8 8 8 6 7 7 8 14 ...
##  $ gcs           : num  15 13 15 15 15 15 15 15 15 3 ...
##  $ cci_num       : num  4 4 4 3 6 5 4 4 5 5 ...
##  $ map           : num  0 8 6 2 4 0 2 4 4 4 ...
##  $ bt            : num  36.4 36.8 36.9 37.1 37.2 ...
##  $ sBP           : num  102 91.2 95 111 125 ...
##  $ dBP           : num  67 54.3 49 68 70 ...
##  $ hr            : num  83 82.3 60 101 121 ...
##  $ bil           : num  7.5 0.5 0.91 2.35 2.49 0.76 1.84 2.18 1.13 3.88 ...
##  $ ast           : num  112 56 37 42 74 18 19 24 31 62 ...
##  $ alt           : num  53 30 19 35 43 13 18 16 25 44 ...
##  $ wbc           : num  9800 14600 3350 12030 6800 ...
##  $ hb            : num  12.2 3.7 7.7 9.8 11.2 10.7 6.6 12.9 10.4 7.7 ...
##  $ plt           : num  69 437 109 107 107 60 140 84 152 178 ...
##  $ tp            : num  5.7 5.6 5.8 5.4 5.9 ...
##  $ alb           : num  2.6 2.1 2.8 3.4 3 3.7 3.4 3.7 3.3 2.7 ...
##  $ eGFR          : num  89.3 65.8 46.6 94.2 90.7 ...
##  $ bun           : num  13.7 42.8 24.2 26.9 7.9 7.6 35 24.9 35.3 23.9 ...
##  $ cre           : num  0.74 0.69 1.16 0.67 0.66 0.54 0.97 0.59 0.66 1.43 ...
##  $ crp           : num  0.1 0.17 0.22 0.04 0.111 ...
##  $ pt            : num  28.5 51.3 60.4 54.8 54 80 70 68 78 69 ...
##  $ aptt          : num  37.8 30.9 30.2 33.4 34.5 ...
##  $ los           : num  6 2 14 5 12 4 1 6 9 16 ...
##  $ meld          : num  20 6 10 9 10 6 10 7 6 17 ...
##  $ pt_id         : int  6 17 24 32 33 37 38 40 42 44 ...
##  $ hosp_num      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_id       : int  1001 1001 1002 1002 1003 1003 1003 1003 1003 1003 ...
##  $ year          : int  2017 2019 2022 2022 2022 2017 2022 2020 2021 2022 ...

#write the csv
#write.csv(imp.mf$ximp, file = "data_after_imputation.csv")

データの確認

str(data_imp_val)

## 'data.frame':    444 obs. of  63 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 2 1 1 1 2 1 2 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": 2 3 2 1 3 1 2 1 1 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 2 2 2 2 1 2 2 2 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 1 2 1 2 1 1 2 2 2 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 2 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 2 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 1 1 1 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ age           : num  47 62 82 57 69 47 50 68 75 53 ...
##  $ bmi           : num  21 20.8 26.9 23.2 23.6 ...
##  $ smoke         : num  270 0 0 0 0 ...
##  $ child_num     : num  11 9 8 8 8 6 7 7 8 14 ...
##  $ gcs           : num  15 13 15 15 15 15 15 15 15 3 ...
##  $ cci_num       : num  4 4 4 3 6 5 4 4 5 5 ...
##  $ map           : num  0 8 6 2 4 0 2 4 4 4 ...
##  $ bt            : num  36.4 36.8 36.9 37.1 37.2 ...
##  $ sBP           : num  102 91.2 95 111 125 ...
##  $ dBP           : num  67 54.3 49 68 70 ...
##  $ hr            : num  83 82.3 60 101 121 ...
##  $ bil           : num  7.5 0.5 0.91 2.35 2.49 0.76 1.84 2.18 1.13 3.88 ...
##  $ ast           : num  112 56 37 42 74 18 19 24 31 62 ...
##  $ alt           : num  53 30 19 35 43 13 18 16 25 44 ...
##  $ wbc           : num  9800 14600 3350 12030 6800 ...
##  $ hb            : num  12.2 3.7 7.7 9.8 11.2 10.7 6.6 12.9 10.4 7.7 ...
##  $ plt           : num  69 437 109 107 107 60 140 84 152 178 ...
##  $ tp            : num  5.7 5.6 5.8 5.4 5.9 ...
##  $ alb           : num  2.6 2.1 2.8 3.4 3 3.7 3.4 3.7 3.3 2.7 ...
##  $ eGFR          : num  89.3 65.8 46.6 94.2 90.7 ...
##  $ bun           : num  13.7 42.8 24.2 26.9 7.9 7.6 35 24.9 35.3 23.9 ...
##  $ cre           : num  0.74 0.69 1.16 0.67 0.66 0.54 0.97 0.59 0.66 1.43 ...
##  $ crp           : num  0.1 0.17 0.22 0.04 0.111 ...
##  $ pt            : num  28.5 51.3 60.4 54.8 54 80 70 68 78 69 ...
##  $ aptt          : num  37.8 30.9 30.2 33.4 34.5 ...
##  $ los           : num  6 2 14 5 12 4 1 6 9 16 ...
##  $ meld          : num  20 6 10 9 10 6 10 7 6 17 ...
##  $ pt_id         : int  6 17 24 32 33 37 38 40 42 44 ...
##  $ hosp_num      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_id       : int  1001 1001 1002 1002 1003 1003 1003 1003 1003 1003 ...
##  $ year          : int  2017 2019 2022 2022 2022 2017 2022 2020 2021 2022 ...

data_imp_val$meld

##   [1] 20.000000  6.000000 10.000000  9.000000 10.000000  6.000000 10.000000
##   [8]  7.000000  6.000000 17.000000 11.000000 14.000000 10.284000 13.000000
##  [15] 16.000000 16.000000 30.000000  6.000000 13.000000 40.000000 15.000000
##  [22] 12.000000 15.000000  6.000000 13.000000  6.000000  9.000000 12.000000
##  [29]  9.000000  8.000000 17.000000  7.000000 13.000000 15.000000  6.000000
##  [36]  9.000000  6.000000 11.000000  7.000000  6.000000 11.000000 14.000000
##  [43] 24.000000 16.000000 14.000000 15.000000 17.000000  6.000000 15.000000
##  [50] 12.000000 20.000000 11.000000  8.000000 13.000000 10.000000  9.000000
##  [57] 10.000000  6.000000  6.000000 17.000000  6.000000 14.000000 15.000000
##  [64]  6.000000 11.000000 15.000000 13.270000 10.000000 12.000000 10.000000
##  [71] 10.000000  8.000000 23.000000 16.000000 17.510000 15.000000 15.000000
##  [78] 14.000000  6.000000 14.000000 11.000000 10.000000  6.000000  6.000000
##  [85] 15.000000 12.000000 10.000000  6.000000  6.000000  6.000000 10.000000
##  [92] 18.000000 13.000000  6.000000  6.000000 11.000000  6.000000 11.000000
##  [99] 19.000000  6.000000 13.000000 13.000000 13.000000  6.000000 15.876667
## [106] 10.270000  8.760000 10.000000 11.000000 16.000000 18.000000  6.000000
## [113]  6.000000 17.000000 24.000000  8.000000 22.000000 26.000000 15.000000
## [120]  8.000000 14.000000 12.000000  6.000000 16.000000  9.000000 14.000000
## [127]  6.000000 23.000000  8.000000 11.000000  6.000000  6.000000  9.000000
## [134] 12.000000  7.000000 24.000000 26.000000 13.720000  6.000000 23.000000
## [141]  7.000000 13.000000 17.000000 16.000000  6.000000  6.000000  7.000000
## [148] 33.000000  6.000000 11.000000 11.000000  6.000000 15.000000  6.000000
## [155] 26.000000 14.100000  8.754286  6.000000  7.000000  6.000000  6.886667
## [162]  6.670000  9.000000  6.000000  9.000000 12.000000 16.000000 12.000000
## [169]  6.000000 12.000000  6.000000  6.000000  6.000000  6.000000 12.000000
## [176] 20.000000 22.000000  6.000000 11.000000  7.000000 10.000000  8.000000
## [183]  6.000000  7.000000  6.000000 14.000000 14.000000 27.000000 14.000000
## [190]  6.000000 14.000000 10.000000  6.000000  6.000000  7.000000 13.000000
## [197] 23.000000  7.000000  6.000000 12.000000  6.000000 32.000000 15.000000
## [204]  6.000000 19.000000  6.000000  8.000000 15.000000  6.000000 18.000000
## [211]  8.180000  9.000000  6.000000  7.000000 17.000000 13.000000  6.000000
## [218]  9.000000  8.000000  8.000000  9.000000 24.000000  7.000000  9.000000
## [225] 12.000000  6.000000  6.000000 13.000000  6.000000  8.000000 12.000000
## [232] 12.000000 15.000000 29.000000 33.000000  8.000000  8.000000 23.000000
## [239] 22.000000 12.000000 12.000000 12.000000  7.000000  6.000000  8.000000
## [246]  6.000000  7.000000  8.000000 19.000000 15.000000 20.000000 14.000000
## [253] 12.000000 16.000000 15.000000  6.000000  6.364286  6.484286  6.000000
## [260]  6.754286  6.754286  6.854286 16.000000  6.000000 16.000000  6.000000
## [267]  6.000000 14.000000  8.942000 12.000000 22.000000 12.000000 10.000000
## [274] 19.000000  6.000000  8.000000  8.000000 11.000000 11.000000  8.000000
## [281]  7.134286  6.000000  6.000000 16.000000 18.000000  8.000000  8.000000
## [288]  8.000000  7.000000  8.000000 14.000000 10.000000 10.000000 11.000000
## [295]  6.000000  8.000000  9.000000 11.000000  8.000000 17.000000  6.000000
## [302]  6.000000  6.000000  6.000000 14.000000 15.000000 12.000000 13.000000
## [309]  9.750000 19.000000 11.000000 15.000000 20.000000  6.000000 21.000000
## [316] 11.000000 16.000000 13.000000  6.000000  6.000000 15.000000 18.000000
## [323] 10.000000 17.000000 12.000000  6.000000 12.000000 15.000000 16.000000
## [330]  6.000000  6.000000  7.000000  7.000000 32.000000  9.000000 24.000000
## [337] 15.000000  6.000000 11.000000  6.000000  6.000000  6.000000  6.000000
## [344]  6.000000  7.000000 25.000000 10.000000  7.000000  9.000000 26.000000
## [351] 19.000000 10.390000 14.610000  6.700000  9.000000 14.000000 11.000000
## [358]  8.000000  6.000000  6.000000 24.000000 15.000000 14.000000 11.000000
## [365]  8.000000  8.000000  7.000000 13.000000 20.000000 15.000000  8.000000
## [372]  6.000000 11.000000 14.000000 11.000000 11.000000  6.000000  6.000000
## [379]  6.000000 23.000000 20.000000  9.000000 29.000000  6.000000  6.000000
## [386] 11.000000  6.000000  8.000000  8.000000  6.000000  6.000000 13.000000
## [393] 19.000000 10.000000  6.000000 15.000000  6.000000 18.000000 21.000000
## [400]  6.000000 10.000000 19.000000 26.000000  6.000000 19.000000 23.000000
## [407] 16.000000  6.000000  8.000000  6.000000 14.000000 19.000000 15.000000
## [414] 14.000000 22.000000 11.000000 11.000000  7.000000 19.730000 15.000000
## [421] 11.000000 18.000000 20.000000  6.000000  6.000000  8.510000  6.000000
## [428]  6.000000  6.000000 15.000000  6.000000  6.000000 16.000000  6.000000
## [435] 11.000000 22.000000 20.000000  7.000000  6.000000  9.000000  6.000000
## [442]  6.000000 12.000000 10.000000

data_imp_val$meld <- round(data_imp_val$meld)

classの変換

val_imp <-
  data_imp_val|> 　
  mutate(
        hosp_id=as.integer(hosp_id),
        pt_id=as.integer(pt_id),
        hosp_num=as.integer(hosp_num),
        year=as.integer(year),
        age=as.integer(age),
        sex= factor(sex, levels = c("M", "F")),
        smoke= as.integer(smoke),
        barthel= factor(barthel, levels = c("0", "1", "2")),
        child_num= as.integer(round(data_imp_val$child_num)),
        child_score=factor(child_score, levels = c("0", "1", "2")),
        gcs=as.integer(gcs),
        cci_num=as.integer(cci_num),
        pad=factor(pad),
        stroke=factor(stroke),
        dimentia=factor(dimentia),
        ch_lung=factor(ch_lung),
        rheumati=factor(rheumati),
        pept_ulcer=factor(pept_ulcer),
        dm=factor(dm),
        dm_compli=factor(dm_compli),
        paralysis=factor(paralysis),
        malignancy=factor(malignancy),
        meta_tumor=factor(meta_tumor),
        aids=factor(aids),
        eGFR30=factor(eGFR30),
        hd=factor(hd),
        hcc=factor(hcc),
        alcohol=factor(alcohol),
        past_rupture=factor(past_rupture),
        antiplate=factor(antiplate),
        anticoag=factor(anticoag),
        antithro=factor(antithro),
        nsaids=factor(nsaids),
        steroid=factor(steroid),
        beta=factor(beta),
        vaso=factor(vaso),
        map= as.integer(map),
        ffp=factor(ffp),
        pc=factor(pc),
        albner=factor(albner),
        sBP= as.integer(sBP),
        dBP= as.integer(dBP),
        hr=as.integer(hr),
        shock=factor(shock),
        los=as.integer(los),
        meld=as.integer(meld)
          )

連続量をカテゴリー化

#新規カテゴリ列を作成
val_imp$age_cate <- ifelse(val_imp$age >= 60, 1, 0)
val_imp$bmi_cate <- ifelse(val_imp$bmi >= 25, 1, 0) # bmiは25以上：1, 25未満：0
val_imp$gcs_cate <- ifelse(val_imp$gcs < 12, 1, 0)
val_imp$cci_cate <- ifelse(val_imp$cci_num >= 6, 1, 0)
val_imp$bt_cate <- ifelse(val_imp$bt >= 37, 1, 0)
val_imp$sBP_cate <- ifelse(val_imp$sBP < 80, 1, 0)
val_imp$dBP_cate <- ifelse(val_imp$dBP < 50, 1, 0)
val_imp$hr_cate <- ifelse(val_imp$hr >= 100, 1, 0)
val_imp$bil_cate <- ifelse(val_imp$bil >= 5, 1, 0)
val_imp$ast_cate <- ifelse(val_imp$ast >= 200, 1, 0)
val_imp$alt_cate <- ifelse(val_imp$alt >= 100, 1, 0)
val_imp$wbc_cate <- ifelse(val_imp$wbc >= 12000, 1, 0)
val_imp$hb_cate <- ifelse(val_imp$hb < 8, 1, 0)
val_imp$plt_cate <- ifelse(val_imp$plt < 100, 1, 0) # pltは100未満：1、100以上：0
val_imp$tp_cate <- ifelse(val_imp$tp < 6, 1, 0)
val_imp$alb_cate <- ifelse(val_imp$alb < 2.8, 1, 0)
val_imp$cre_cate <- ifelse(val_imp$cre >= 1.5, 1, 0)
val_imp$crp_cate <- ifelse(val_imp$crp >= 2, 1, 0)
val_imp$pt_cate <- ifelse(val_imp$pt < 50, 1, 0)
val_imp$aptt_cate <- ifelse(val_imp$aptt >= 50, 1, 0) # apttは50以上：1, 50未満：0

str(val_imp)

## 'data.frame':    444 obs. of  83 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 2 1 1 1 2 1 2 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": 2 3 2 1 3 1 2 1 1 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 2 2 2 2 1 2 2 2 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 1 2 1 2 1 1 2 2 2 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 2 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 2 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 1 1 1 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ age           : int  47 62 82 57 69 47 50 68 75 53 ...
##  $ bmi           : num  21 20.8 26.9 23.2 23.6 ...
##  $ smoke         : int  270 0 0 0 0 135 435 0 0 0 ...
##  $ child_num     : int  11 9 8 8 8 6 7 7 8 14 ...
##  $ gcs           : int  15 13 15 15 15 15 15 15 15 3 ...
##  $ cci_num       : int  4 4 4 3 6 5 4 4 5 5 ...
##  $ map           : int  0 8 6 2 4 0 2 4 4 4 ...
##  $ bt            : num  36.4 36.8 36.9 37.1 37.2 ...
##  $ sBP           : int  102 91 95 111 125 116 102 96 97 71 ...
##  $ dBP           : int  67 54 49 68 70 84 88 55 54 49 ...
##  $ hr            : int  83 82 60 101 121 56 79 72 75 56 ...
##  $ bil           : num  7.5 0.5 0.91 2.35 2.49 0.76 1.84 2.18 1.13 3.88 ...
##  $ ast           : num  112 56 37 42 74 18 19 24 31 62 ...
##  $ alt           : num  53 30 19 35 43 13 18 16 25 44 ...
##  $ wbc           : num  9800 14600 3350 12030 6800 ...
##  $ hb            : num  12.2 3.7 7.7 9.8 11.2 10.7 6.6 12.9 10.4 7.7 ...
##  $ plt           : num  69 437 109 107 107 60 140 84 152 178 ...
##  $ tp            : num  5.7 5.6 5.8 5.4 5.9 ...
##  $ alb           : num  2.6 2.1 2.8 3.4 3 3.7 3.4 3.7 3.3 2.7 ...
##  $ eGFR          : num  89.3 65.8 46.6 94.2 90.7 ...
##  $ bun           : num  13.7 42.8 24.2 26.9 7.9 7.6 35 24.9 35.3 23.9 ...
##  $ cre           : num  0.74 0.69 1.16 0.67 0.66 0.54 0.97 0.59 0.66 1.43 ...
##  $ crp           : num  0.1 0.17 0.22 0.04 0.111 ...
##  $ pt            : num  28.5 51.3 60.4 54.8 54 80 70 68 78 69 ...
##  $ aptt          : num  37.8 30.9 30.2 33.4 34.5 ...
##  $ los           : int  6 2 14 5 12 4 1 6 9 16 ...
##  $ meld          : int  20 6 10 9 10 6 10 7 6 17 ...
##  $ pt_id         : int  6 17 24 32 33 37 38 40 42 44 ...
##  $ hosp_num      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_id       : int  1001 1001 1002 1002 1003 1003 1003 1003 1003 1003 ...
##  $ year          : int  2017 2019 2022 2022 2022 2017 2022 2020 2021 2022 ...
##  $ age_cate      : num  0 1 1 0 1 0 0 1 1 0 ...
##  $ bmi_cate      : num  0 0 1 0 0 1 1 1 0 1 ...
##  $ gcs_cate      : num  0 0 0 0 0 0 0 0 0 1 ...
##  $ cci_cate      : num  0 0 0 0 1 0 0 0 0 0 ...
##  $ bt_cate       : num  0 0 0 1 1 0 0 1 1 0 ...
##  $ sBP_cate      : num  0 0 0 0 0 0 0 0 0 1 ...
##  $ dBP_cate      : num  0 0 1 0 0 0 0 0 0 1 ...
##  $ hr_cate       : num  0 0 0 1 1 0 0 0 0 0 ...
##  $ bil_cate      : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ ast_cate      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ alt_cate      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ wbc_cate      : num  0 1 0 1 0 0 0 0 0 0 ...
##  $ hb_cate       : num  0 1 1 0 0 0 1 0 0 1 ...
##  $ plt_cate      : num  1 0 0 0 0 1 0 1 0 0 ...
##  $ tp_cate       : num  1 1 1 1 1 0 1 0 0 0 ...
##  $ alb_cate      : num  1 1 0 0 0 0 0 0 0 1 ...
##  $ cre_cate      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ crp_cate      : num  0 0 0 0 0 0 0 0 0 1 ...
##  $ pt_cate       : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ aptt_cate     : num  0 0 0 0 0 0 0 0 0 0 ...

tableの作成

Createtableone

col_fact_cate=c("sex","barthel","child_score","pad","stroke","dimentia","ch_lung","rheumati","pept_ulcer","dm","dm_compli","paralysis","malignancy","meta_tumor","aids","eGFR30","hd","hcc","alcohol","past_rupture","antiplate","anticoag","antithro","nsaids","steroid","beta", "vaso","ffp","pc", "albner","shock","hosp_mortality","age_cate","bmi_cate","gcs_cate","cci_cate","bt_cate","sBP_cate","dBP_cate","hr_cate","bil_cate","ast_cate","alt_cate","wbc_cate","hb_cate","plt_cate","tp_cate","alb_cate","cre_cate","crp_cate","pt_cate","aptt_cate")

# Create your table
val_imp %>%  
  select(c(col_fact_cate)) %>% 
  CreateTableOne(vars = c(col_fact_cate), strata="hosp_mortality",factorVars = col_fact_cate, addOverall = T) -> tableone_dev_imp_cate


# Print your table
print(tableone_dev_imp_cate, smd = TRUE, missing = TRUE, test = TRUE, explain = TRUE) 

##                         Stratified by hosp_mortality
##                          Overall      0            1           p      test
##   n                      444          399          45                     
##   sex = F (%)            115 ( 25.9)  103 ( 25.8)  12 ( 26.7)   1.000     
##   barthel (%)                                                  <0.001     
##      0                   174 ( 39.2)  170 ( 42.6)   4 (  8.9)             
##      1                   141 ( 31.8)  130 ( 32.6)  11 ( 24.4)             
##      2                   129 ( 29.1)   99 ( 24.8)  30 ( 66.7)             
##   child_score (%)                                              <0.001     
##      0                    83 ( 18.7)   81 ( 20.3)   2 (  4.4)             
##      1                   240 ( 54.1)  227 ( 56.9)  13 ( 28.9)             
##      2                   121 ( 27.3)   91 ( 22.8)  30 ( 66.7)             
##   pad = 1 (%)              1 (  0.2)    1 (  0.3)   0 (  0.0)   1.000     
##   stroke = 1 (%)          11 (  2.5)    8 (  2.0)   3 (  6.7)   0.161     
##   dimentia = 1 (%)         6 (  1.4)    5 (  1.3)   1 (  2.2)   1.000     
##   ch_lung = 1 (%)          4 (  0.9)    3 (  0.8)   1 (  2.2)   0.875     
##   rheumati = 0 (%)       444 (100.0)  399 (100.0)  45 (100.0)      NA     
##   pept_ulcer = 1 (%)      42 (  9.5)   40 ( 10.0)   2 (  4.4)   0.345     
##   dm = 1 (%)             104 ( 23.4)   96 ( 24.1)   8 ( 17.8)   0.449     
##   dm_compli = 1 (%)        7 (  1.6)    6 (  1.5)   1 (  2.2)   1.000     
##   paralysis = 0 (%)      444 (100.0)  399 (100.0)  45 (100.0)      NA     
##   malignancy = 1 (%)      57 ( 12.8)   50 ( 12.5)   7 ( 15.6)   0.734     
##   meta_tumor = 1 (%)      11 (  2.5)    7 (  1.8)   4 (  8.9)   0.016     
##   aids = 0 (%)           444 (100.0)  399 (100.0)  45 (100.0)      NA     
##   eGFR30 = 1 (%)          36 (  8.1)   22 (  5.5)  14 ( 31.1)  <0.001     
##   hd = 1 (%)              10 (  2.3)    7 (  1.8)   3 (  6.7)   0.115     
##   hcc = 1 (%)             64 ( 14.4)   56 ( 14.0)   8 ( 17.8)   0.650     
##   alcohol = 1 (%)        241 ( 54.3)  212 ( 53.1)  29 ( 64.4)   0.198     
##   past_rupture = 1 (%)   121 ( 27.3)  115 ( 28.8)   6 ( 13.3)   0.042     
##   antiplate = 1 (%)        3 (  0.7)    2 (  0.5)   1 (  2.2)   0.707     
##   anticoag = 1 (%)         3 (  0.7)    3 (  0.8)   0 (  0.0)   1.000     
##   antithro = 1 (%)         6 (  1.4)    5 (  1.3)   1 (  2.2)   1.000     
##   nsaids = 1 (%)           4 (  0.9)    4 (  1.0)   0 (  0.0)   1.000     
##   steroid = 1 (%)          3 (  0.7)    3 (  0.8)   0 (  0.0)   1.000     
##   beta = 1 (%)            40 (  9.0)   40 ( 10.0)   0 (  0.0)   0.051     
##   vaso = 1 (%)            27 (  6.1)   18 (  4.5)   9 ( 20.0)  <0.001     
##   ffp = 1 (%)            154 ( 34.7)  126 ( 31.6)  28 ( 62.2)  <0.001     
##   pc = 1 (%)              11 (  2.5)    6 (  1.5)   5 ( 11.1)   0.001     
##   albner = 1 (%)          33 (  7.4)   26 (  6.5)   7 ( 15.6)   0.059     
##   shock = 1 (%)          196 ( 44.1)  153 ( 38.3)  43 ( 95.6)  <0.001     
##   hosp_mortality = 1 (%)  45 ( 10.1)    0 (  0.0)  45 (100.0)  <0.001     
##   age_cate = 1 (%)       226 ( 50.9)  200 ( 50.1)  26 ( 57.8)   0.414     
##   bmi_cate = 1 (%)       135 ( 30.4)  124 ( 31.1)  11 ( 24.4)   0.456     
##   gcs_cate = 1 (%)        10 (  2.3)    8 (  2.0)   2 (  4.4)   0.606     
##   cci_cate = 1 (%)        71 ( 16.0)   62 ( 15.5)   9 ( 20.0)   0.576     
##   bt_cate = 1 (%)        150 ( 33.8)  135 ( 33.8)  15 ( 33.3)   1.000     
##   sBP_cate = 1 (%)       102 ( 23.0)   68 ( 17.0)  34 ( 75.6)  <0.001     
##   dBP_cate = 1 (%)       152 ( 34.2)  118 ( 29.6)  34 ( 75.6)  <0.001     
##   hr_cate = 1 (%)        128 ( 28.8)  102 ( 25.6)  26 ( 57.8)  <0.001     
##   bil_cate = 1 (%)        50 ( 11.3)   38 (  9.5)  12 ( 26.7)   0.001     
##   ast_cate = 1 (%)        30 (  6.8)   21 (  5.3)   9 ( 20.0)   0.001     
##   alt_cate = 1 (%)        25 (  5.6)   18 (  4.5)   7 ( 15.6)   0.007     
##   wbc_cate = 1 (%)        91 ( 20.5)   75 ( 18.8)  16 ( 35.6)   0.014     
##   hb_cate = 1 (%)        164 ( 36.9)  138 ( 34.6)  26 ( 57.8)   0.004     
##   plt_cate = 1 (%)       207 ( 46.6)  186 ( 46.6)  21 ( 46.7)   1.000     
##   tp_cate = 1 (%)        185 ( 41.7)  155 ( 38.8)  30 ( 66.7)   0.001     
##   alb_cate = 1 (%)       176 ( 39.6)  140 ( 35.1)  36 ( 80.0)  <0.001     
##   cre_cate = 1 (%)        51 ( 11.5)   33 (  8.3)  18 ( 40.0)  <0.001     
##   crp_cate = 1 (%)        47 ( 10.6)   34 (  8.5)  13 ( 28.9)  <0.001     
##   pt_cate = 1 (%)        138 ( 31.1)  107 ( 26.8)  31 ( 68.9)  <0.001     
##   aptt_cate = 1 (%)       27 (  6.1)   11 (  2.8)  16 ( 35.6)  <0.001     
##                         Stratified by hosp_mortality
##                          SMD    Missing
##   n                                    
##   sex = F (%)             0.019 0.0    
##   barthel (%)             1.037 0.0    
##      0                                 
##      1                                 
##      2                                 
##   child_score (%)         1.013 0.0    
##      0                                 
##      1                                 
##      2                                 
##   pad = 1 (%)             0.071 0.0    
##   stroke = 1 (%)          0.230 0.0    
##   dimentia = 1 (%)        0.074 0.0    
##   ch_lung = 1 (%)         0.122 0.0    
##   rheumati = 0 (%)       <0.001 0.0    
##   pept_ulcer = 1 (%)      0.217 0.0    
##   dm = 1 (%)              0.155 0.0    
##   dm_compli = 1 (%)       0.053 0.0    
##   paralysis = 0 (%)      <0.001 0.0    
##   malignancy = 1 (%)      0.087 0.0    
##   meta_tumor = 1 (%)      0.322 0.0    
##   aids = 0 (%)           <0.001 0.0    
##   eGFR30 = 1 (%)          0.701 0.0    
##   hd = 1 (%)              0.246 0.0    
##   hcc = 1 (%)             0.102 0.0    
##   alcohol = 1 (%)         0.231 0.0    
##   past_rupture = 1 (%)    0.387 0.0    
##   antiplate = 1 (%)       0.149 0.0    
##   anticoag = 1 (%)        0.123 0.0    
##   antithro = 1 (%)        0.074 0.0    
##   nsaids = 1 (%)          0.142 0.0    
##   steroid = 1 (%)         0.123 0.0    
##   beta = 1 (%)            0.472 0.0    
##   vaso = 1 (%)            0.486 0.0    
##   ffp = 1 (%)             0.645 0.0    
##   pc = 1 (%)              0.403 0.0    
##   albner = 1 (%)          0.292 0.0    
##   shock = 1 (%)           1.532 0.0    
##   hosp_mortality = 1 (%)    NaN 0.0    
##   age_cate = 1 (%)        0.154 0.0    
##   bmi_cate = 1 (%)        0.149 0.0    
##   gcs_cate = 1 (%)        0.138 0.0    
##   cci_cate = 1 (%)        0.117 0.0    
##   bt_cate = 1 (%)         0.011 0.0    
##   sBP_cate = 1 (%)        1.449 0.0    
##   dBP_cate = 1 (%)        1.037 0.0    
##   hr_cate = 1 (%)         0.691 0.0    
##   bil_cate = 1 (%)        0.457 0.0    
##   ast_cate = 1 (%)        0.455 0.0    
##   alt_cate = 1 (%)        0.374 0.0    
##   wbc_cate = 1 (%)        0.384 0.0    
##   hb_cate = 1 (%)         0.478 0.0    
##   plt_cate = 1 (%)        0.001 0.0    
##   tp_cate = 1 (%)         0.580 0.0    
##   alb_cate = 1 (%)        1.020 0.0    
##   cre_cate = 1 (%)        0.798 0.0    
##   crp_cate = 1 (%)        0.541 0.0    
##   pt_cate = 1 (%)         0.929 0.0    
##   aptt_cate = 1 (%)       0.917 0.0

tbl_summary

# specify your data and variables
tbl_summary(data = val_imp, 
            by = "hosp_mortality",
            type = list(gcs ~ "continuous", year ~ "categorical"),
            statistic = all_continuous() ~ "{median} ({p25}, {p75})",
            digits = all_continuous() ~ c(0, 2))

Characteristic	0, N = 3991	1, N = 451
sex
M	296 (74%)	33 (73%)
F	103 (26%)	12 (27%)
barthel
0	170 (43%)	4 (8.9%)
1	130 (33%)	11 (24%)
2	99 (25%)	30 (67%)
child_score
0	81 (20%)	2 (4.4%)
1	227 (57%)	13 (29%)
2	91 (23%)	30 (67%)
pad
0	398 (100%)	45 (100%)
1	1 (0.3%)	0 (0%)
stroke
0	391 (98%)	42 (93%)
1	8 (2.0%)	3 (6.7%)
dimentia
0	394 (99%)	44 (98%)
1	5 (1.3%)	1 (2.2%)
ch_lung
0	396 (99%)	44 (98%)
1	3 (0.8%)	1 (2.2%)
rheumati
0	399 (100%)	45 (100%)
pept_ulcer
0	359 (90%)	43 (96%)
1	40 (10%)	2 (4.4%)
dm
0	303 (76%)	37 (82%)
1	96 (24%)	8 (18%)
dm_compli
0	393 (98%)	44 (98%)
1	6 (1.5%)	1 (2.2%)
paralysis
0	399 (100%)	45 (100%)
malignancy
0	349 (87%)	38 (84%)
1	50 (13%)	7 (16%)
meta_tumor
0	392 (98%)	41 (91%)
1	7 (1.8%)	4 (8.9%)
aids
0	399 (100%)	45 (100%)
eGFR30
0	377 (94%)	31 (69%)
1	22 (5.5%)	14 (31%)
hd
0	392 (98%)	42 (93%)
1	7 (1.8%)	3 (6.7%)
hcc
0	343 (86%)	37 (82%)
1	56 (14%)	8 (18%)
alcohol
0	187 (47%)	16 (36%)
1	212 (53%)	29 (64%)
past_rupture
0	284 (71%)	39 (87%)
1	115 (29%)	6 (13%)
antiplate
0	397 (99%)	44 (98%)
1	2 (0.5%)	1 (2.2%)
anticoag
0	396 (99%)	45 (100%)
1	3 (0.8%)	0 (0%)
antithro
0	394 (99%)	44 (98%)
1	5 (1.3%)	1 (2.2%)
nsaids
0	395 (99%)	45 (100%)
1	4 (1.0%)	0 (0%)
steroid
0	396 (99%)	45 (100%)
1	3 (0.8%)	0 (0%)
beta
0	359 (90%)	45 (100%)
1	40 (10%)	0 (0%)
vaso
0	381 (95%)	36 (80%)
1	18 (4.5%)	9 (20%)
ffp
0	273 (68%)	17 (38%)
1	126 (32%)	28 (62%)
pc
0	393 (98%)	40 (89%)
1	6 (1.5%)	5 (11%)
albner
0	373 (93%)	38 (84%)
1	26 (6.5%)	7 (16%)
shock
0	246 (62%)	2 (4.4%)
1	153 (38%)	43 (96%)
age	60 (50.00, 70)	64 (52.00, 70)
bmi	23 (20.68, 26)	23 (22.15, 25)
smoke	0 (0.00, 326)	0 (0.00, 274)
child_num	8 (7.00, 9)	11 (9.00, 12)
gcs	15 (15.00, 15)	15 (14.00, 15)
cci_num	4 (4.00, 5)	4 (4.00, 5)
map	2 (0.00, 4)	4 (4.00, 8)
bt	37 (36.50, 37)	37 (36.30, 37)
sBP	93 (82.50, 101)	68 (56.00, 79)
dBP	56 (48.00, 64)	43 (34.00, 49)
hr	83 (72.00, 100)	105 (91.00, 117)
bil	2 (1.00, 3)	3 (1.70, 5)
ast	48 (31.50, 84)	81 (40.00, 171)
alt	29 (19.00, 42)	34 (19.00, 69)
wbc	7,960 (5,700.00, 10,760)	10,000 (8,900.00, 13,800)
hb	9 (7.30, 11)	8 (6.90, 9)
plt	102 (76.00, 140)	109 (56.00, 146)
tp	6 (5.70, 7)	6 (5.00, 6)
alb	3 (2.55, 3)	2 (1.80, 3)
eGFR	72 (54.21, 93)	42 (28.38, 57)
bun	24 (16.37, 36)	33 (20.20, 47)
cre	1 (0.64, 1)	1 (0.94, 2)
crp	0 (0.12, 1)	1 (0.19, 2)
pt	61 (49.00, 73)	40 (31.00, 53)
aptt	32 (29.70, 36)	43 (35.50, 56)
los	7 (5.00, 13)	6 (2.00, 12)
meld	9 (6.00, 14)	20 (14.00, 24)
pt_id	488 (232.50, 640)	510 (299.00, 738)
hosp_num
1	326 (82%)	39 (87%)
2	46 (12%)	4 (8.9%)
3	12 (3.0%)	1 (2.2%)
4	10 (2.5%)	0 (0%)
5	3 (0.8%)	1 (2.2%)
6	2 (0.5%)	0 (0%)
hosp_id	1,022 (1,005.00, 1,024)	1,022 (1,006.00, 1,062)
year
2017	57 (14%)	7 (16%)
2018	53 (13%)	9 (20%)
2019	63 (16%)	9 (20%)
2020	74 (19%)	8 (18%)
2021	76 (19%)	7 (16%)
2022	76 (19%)	5 (11%)
age_cate	200 (50%)	26 (58%)
bmi_cate	124 (31%)	11 (24%)
gcs_cate	8 (2.0%)	2 (4.4%)
cci_cate	62 (16%)	9 (20%)
bt_cate	135 (34%)	15 (33%)
sBP_cate	68 (17%)	34 (76%)
dBP_cate	118 (30%)	34 (76%)
hr_cate	102 (26%)	26 (58%)
bil_cate	38 (9.5%)	12 (27%)
ast_cate	21 (5.3%)	9 (20%)
alt_cate	18 (4.5%)	7 (16%)
wbc_cate	75 (19%)	16 (36%)
hb_cate	138 (35%)	26 (58%)
plt_cate	186 (47%)	21 (47%)
tp_cate	155 (39%)	30 (67%)
alb_cate	140 (35%)	36 (80%)
cre_cate	33 (8.3%)	18 (40%)
crp_cate	34 (8.5%)	13 (29%)
pt_cate	107 (27%)	31 (69%)
aptt_cate	11 (2.8%)	16 (36%)
1 n (%); Median (IQR)

barthelはダミー変数を追加する。

# ダミー変数を作成
dummy_vars_val <- model.matrix(~barthel, data = val_imp)

# データフレームに追加
val_imp <- cbind(val_imp, dummy_vars_val)

intercept列は抜く

val_imp <- val_imp[ , !(names(val_imp) %in% "(Intercept)")]

確認

str(val_imp)

## 'data.frame':    444 obs. of  85 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 2 1 1 1 2 1 2 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": 2 3 2 1 3 1 2 1 1 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 2 2 2 2 1 2 2 2 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 1 2 1 2 1 1 2 2 2 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 2 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 1 2 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 1 1 1 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_mortality: Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ age           : int  47 62 82 57 69 47 50 68 75 53 ...
##  $ bmi           : num  21 20.8 26.9 23.2 23.6 ...
##  $ smoke         : int  270 0 0 0 0 135 435 0 0 0 ...
##  $ child_num     : int  11 9 8 8 8 6 7 7 8 14 ...
##  $ gcs           : int  15 13 15 15 15 15 15 15 15 3 ...
##  $ cci_num       : int  4 4 4 3 6 5 4 4 5 5 ...
##  $ map           : int  0 8 6 2 4 0 2 4 4 4 ...
##  $ bt            : num  36.4 36.8 36.9 37.1 37.2 ...
##  $ sBP           : int  102 91 95 111 125 116 102 96 97 71 ...
##  $ dBP           : int  67 54 49 68 70 84 88 55 54 49 ...
##  $ hr            : int  83 82 60 101 121 56 79 72 75 56 ...
##  $ bil           : num  7.5 0.5 0.91 2.35 2.49 0.76 1.84 2.18 1.13 3.88 ...
##  $ ast           : num  112 56 37 42 74 18 19 24 31 62 ...
##  $ alt           : num  53 30 19 35 43 13 18 16 25 44 ...
##  $ wbc           : num  9800 14600 3350 12030 6800 ...
##  $ hb            : num  12.2 3.7 7.7 9.8 11.2 10.7 6.6 12.9 10.4 7.7 ...
##  $ plt           : num  69 437 109 107 107 60 140 84 152 178 ...
##  $ tp            : num  5.7 5.6 5.8 5.4 5.9 ...
##  $ alb           : num  2.6 2.1 2.8 3.4 3 3.7 3.4 3.7 3.3 2.7 ...
##  $ eGFR          : num  89.3 65.8 46.6 94.2 90.7 ...
##  $ bun           : num  13.7 42.8 24.2 26.9 7.9 7.6 35 24.9 35.3 23.9 ...
##  $ cre           : num  0.74 0.69 1.16 0.67 0.66 0.54 0.97 0.59 0.66 1.43 ...
##  $ crp           : num  0.1 0.17 0.22 0.04 0.111 ...
##  $ pt            : num  28.5 51.3 60.4 54.8 54 80 70 68 78 69 ...
##  $ aptt          : num  37.8 30.9 30.2 33.4 34.5 ...
##  $ los           : int  6 2 14 5 12 4 1 6 9 16 ...
##  $ meld          : int  20 6 10 9 10 6 10 7 6 17 ...
##  $ pt_id         : int  6 17 24 32 33 37 38 40 42 44 ...
##  $ hosp_num      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_id       : int  1001 1001 1002 1002 1003 1003 1003 1003 1003 1003 ...
##  $ year          : int  2017 2019 2022 2022 2022 2017 2022 2020 2021 2022 ...
##  $ age_cate      : num  0 1 1 0 1 0 0 1 1 0 ...
##  $ bmi_cate      : num  0 0 1 0 0 1 1 1 0 1 ...
##  $ gcs_cate      : num  0 0 0 0 0 0 0 0 0 1 ...
##  $ cci_cate      : num  0 0 0 0 1 0 0 0 0 0 ...
##  $ bt_cate       : num  0 0 0 1 1 0 0 1 1 0 ...
##  $ sBP_cate      : num  0 0 0 0 0 0 0 0 0 1 ...
##  $ dBP_cate      : num  0 0 1 0 0 0 0 0 0 1 ...
##  $ hr_cate       : num  0 0 0 1 1 0 0 0 0 0 ...
##  $ bil_cate      : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ ast_cate      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ alt_cate      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ wbc_cate      : num  0 1 0 1 0 0 0 0 0 0 ...
##  $ hb_cate       : num  0 1 1 0 0 0 1 0 0 1 ...
##  $ plt_cate      : num  1 0 0 0 0 1 0 1 0 0 ...
##  $ tp_cate       : num  1 1 1 1 1 0 1 0 0 0 ...
##  $ alb_cate      : num  1 1 0 0 0 0 0 0 0 1 ...
##  $ cre_cate      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ crp_cate      : num  0 0 0 0 0 0 0 0 0 1 ...
##  $ pt_cate       : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ aptt_cate     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ barthel1      : num  1 0 1 0 0 0 1 0 0 0 ...
##  $ barthel2      : num  0 1 0 0 1 0 0 0 0 1 ...

開発データにもbaerthelはダミー変数を追加する。

# ダミー変数を作成
dummy_vars <- model.matrix(~barthel, data = dev_imp)

# データフレームに追加
dev_imp <- cbind(dev_imp, dummy_vars)

確認

str(dev_imp)

## 'data.frame':    536 obs. of  88 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 1 2 1 2 1 2 1 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": 3 3 1 2 3 1 1 1 3 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 1 2 2 2 2 2 2 3 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 2 1 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 1 2 2 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 1 2 2 2 ...
##  $ hosp_mortality: num  0 0 0 0 0 0 0 0 1 1 ...
##  $ age           : int  50 80 59 44 67 65 49 73 69 62 ...
##  $ bmi           : num  23.4 25.3 23.5 14.5 21.9 ...
##  $ smoke         : int  0 0 0 240 0 0 217 1000 0 0 ...
##  $ child_num     : int  11 6 9 8 8 8 7 9 15 11 ...
##  $ gcs           : int  15 15 15 15 15 15 15 15 6 15 ...
##  $ cci_num       : int  4 4 3 4 4 3 4 4 4 4 ...
##  $ map           : int  0 2 0 6 2 2 0 4 14 6 ...
##  $ bt            : num  36.4 36.8 35.9 36 36.6 38.4 37 37 35.5 36.6 ...
##  $ sBP           : int  78 88 100 69 66 84 132 90 52 58 ...
##  $ dBP           : int  48 49 56 44 40 46 69 54 37 37 ...
##  $ hr            : int  118 72 110 104 72 127 83 114 98 106 ...
##  $ bil           : num  2.2 1.2 3.1 3.4 1.2 2.4 1.2 2.2 8.7 2.1 ...
##  $ ast           : num  217 31 60 129 52 90 154 55 96 121 ...
##  $ alt           : num  63 22 40 46 36 19 109 20 87 63 ...
##  $ wbc           : num  7400 5000 7800 9100 3900 8000 7900 11100 12800 8300 ...
##  $ hb            : num  6.9 10.8 9.7 10.7 6.3 9.8 13.5 5.6 6 9.8 ...
##  $ plt           : num  115 77 74 162 63 93 132 124 168 84 ...
##  $ tp            : num  6.3 5.6 6.4 6.1 5.1 7.2 7.6 4.9 5.3 6.3 ...
##  $ alb           : num  2.2 3.2 2.8 2.9 2.8 3.2 4 2.3 1.2 2.5 ...
##  $ eGFR          : num  58 57.7 63.7 112.4 123.6 ...
##  $ bun           : num  13.2 41.5 26.3 2.9 27.4 15.8 15.5 27 63.2 13.8 ...
##  $ cre           : num  1.08 0.96 0.72 0.61 0.38 0.44 0.4 0.97 2.07 0.87 ...
##  $ crp           : num  0.92 0.29 0.68 0.29 0.29 ...
##  $ pt            : num  37.8 55 46.7 37.8 74.6 45.9 49.7 45.9 37.2 54 ...
##  $ aptt          : num  29.4 29 27.2 35.3 30.1 27.6 32.6 27.5 36 29.1 ...
##  $ los           : int  12 7 0 10 3 2 1 8 0 16 ...
##  $ meld          : int  16 11 13 14 6 8 6 15 29 12 ...
##  $ pt_id         : int  1 2 3 4 5 7 8 9 10 11 ...
##  $ hosp_num      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_id       : int  1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
##  $ year          : int  2012 2011 2010 2011 2010 2010 2010 2010 2011 2012 ...
##  $ age_cate      : num  0 1 0 0 1 1 0 1 1 1 ...
##  $ bmi_cate      : num  0 1 0 0 0 0 0 0 0 0 ...
##  $ gcs_cate      : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ cci_cate      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ bt_cate       : num  0 0 0 0 0 1 1 1 0 0 ...
##  $ sBP_cate      : num  1 0 0 1 1 0 0 0 1 1 ...
##  $ dBP_cate      : num  1 1 0 1 1 1 0 0 1 1 ...
##  $ hr_cate       : num  1 0 1 1 0 1 0 1 0 1 ...
##  $ bil_cate      : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ ast_cate      : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ alt_cate      : num  0 0 0 0 0 0 1 0 0 0 ...
##  $ wbc_cate      : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ hb_cate       : num  1 0 0 0 1 0 0 1 1 0 ...
##  $ plt_cate      : num  0 1 1 0 1 1 0 0 0 1 ...
##  $ tp_cate       : num  0 1 0 0 1 0 0 1 1 0 ...
##  $ alb_cate      : num  1 0 0 0 0 0 0 1 1 1 ...
##  $ cre_cate      : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ crp_cate      : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ pt_cate       : num  1 0 1 1 0 1 1 1 1 0 ...
##  $ aptt_cate     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ fitted        : num  0.6193 0.0131 0.0131 0.1354 0.1354 ...
##  $ diff2         : num  0.383566 0.000173 0.000173 0.018334 0.018334 ...
##  $ (Intercept)   : num  1 1 1 1 1 1 1 1 1 1 ...
##  $ barthel1      : num  0 0 0 1 0 0 0 0 0 0 ...
##  $ barthel2      : num  1 1 0 0 1 0 0 0 1 1 ...

intercept列は抜く

dev_imp <- dev_imp[ , !(names(dev_imp) %in% "(Intercept)")]
dev_imp <- dev_imp[ , !(names(dev_imp) %in% "fitted")]
dev_imp <- dev_imp[ , !(names(dev_imp) %in% "diff2")]

確認

str(dev_imp)

## 'data.frame':    536 obs. of  85 variables:
##  $ sex           : Factor w/ 2 levels "M","F": 1 1 2 1 2 1 2 1 2 1 ...
##  $ barthel       : Factor w/ 3 levels "0","1","2": 3 3 1 2 3 1 1 1 3 3 ...
##  $ child_score   : Factor w/ 3 levels "0","1","2": 3 1 2 2 2 2 2 2 3 3 ...
##  $ pad           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ stroke        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dimentia      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ ch_lung       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ rheumati      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ pept_ulcer    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ dm            : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
##  $ dm_compli     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ paralysis     : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ malignancy    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ meta_tumor    : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ aids          : Factor w/ 1 level "0": 1 1 1 1 1 1 1 1 1 1 ...
##  $ eGFR30        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ hd            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ hcc           : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ alcohol       : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 2 1 1 ...
##  $ past_rupture  : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antiplate     : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ anticoag      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ antithro      : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ nsaids        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ steroid       : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ beta          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ vaso          : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ ffp           : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 1 2 2 2 ...
##  $ pc            : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
##  $ albner        : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
##  $ shock         : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 1 2 2 2 ...
##  $ hosp_mortality: num  0 0 0 0 0 0 0 0 1 1 ...
##  $ age           : int  50 80 59 44 67 65 49 73 69 62 ...
##  $ bmi           : num  23.4 25.3 23.5 14.5 21.9 ...
##  $ smoke         : int  0 0 0 240 0 0 217 1000 0 0 ...
##  $ child_num     : int  11 6 9 8 8 8 7 9 15 11 ...
##  $ gcs           : int  15 15 15 15 15 15 15 15 6 15 ...
##  $ cci_num       : int  4 4 3 4 4 3 4 4 4 4 ...
##  $ map           : int  0 2 0 6 2 2 0 4 14 6 ...
##  $ bt            : num  36.4 36.8 35.9 36 36.6 38.4 37 37 35.5 36.6 ...
##  $ sBP           : int  78 88 100 69 66 84 132 90 52 58 ...
##  $ dBP           : int  48 49 56 44 40 46 69 54 37 37 ...
##  $ hr            : int  118 72 110 104 72 127 83 114 98 106 ...
##  $ bil           : num  2.2 1.2 3.1 3.4 1.2 2.4 1.2 2.2 8.7 2.1 ...
##  $ ast           : num  217 31 60 129 52 90 154 55 96 121 ...
##  $ alt           : num  63 22 40 46 36 19 109 20 87 63 ...
##  $ wbc           : num  7400 5000 7800 9100 3900 8000 7900 11100 12800 8300 ...
##  $ hb            : num  6.9 10.8 9.7 10.7 6.3 9.8 13.5 5.6 6 9.8 ...
##  $ plt           : num  115 77 74 162 63 93 132 124 168 84 ...
##  $ tp            : num  6.3 5.6 6.4 6.1 5.1 7.2 7.6 4.9 5.3 6.3 ...
##  $ alb           : num  2.2 3.2 2.8 2.9 2.8 3.2 4 2.3 1.2 2.5 ...
##  $ eGFR          : num  58 57.7 63.7 112.4 123.6 ...
##  $ bun           : num  13.2 41.5 26.3 2.9 27.4 15.8 15.5 27 63.2 13.8 ...
##  $ cre           : num  1.08 0.96 0.72 0.61 0.38 0.44 0.4 0.97 2.07 0.87 ...
##  $ crp           : num  0.92 0.29 0.68 0.29 0.29 ...
##  $ pt            : num  37.8 55 46.7 37.8 74.6 45.9 49.7 45.9 37.2 54 ...
##  $ aptt          : num  29.4 29 27.2 35.3 30.1 27.6 32.6 27.5 36 29.1 ...
##  $ los           : int  12 7 0 10 3 2 1 8 0 16 ...
##  $ meld          : int  16 11 13 14 6 8 6 15 29 12 ...
##  $ pt_id         : int  1 2 3 4 5 7 8 9 10 11 ...
##  $ hosp_num      : int  1 1 1 1 1 1 1 1 1 1 ...
##  $ hosp_id       : int  1001 1001 1001 1001 1001 1001 1001 1001 1001 1001 ...
##  $ year          : int  2012 2011 2010 2011 2010 2010 2010 2010 2011 2012 ...
##  $ age_cate      : num  0 1 0 0 1 1 0 1 1 1 ...
##  $ bmi_cate      : num  0 1 0 0 0 0 0 0 0 0 ...
##  $ gcs_cate      : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ cci_cate      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ bt_cate       : num  0 0 0 0 0 1 1 1 0 0 ...
##  $ sBP_cate      : num  1 0 0 1 1 0 0 0 1 1 ...
##  $ dBP_cate      : num  1 1 0 1 1 1 0 0 1 1 ...
##  $ hr_cate       : num  1 0 1 1 0 1 0 1 0 1 ...
##  $ bil_cate      : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ ast_cate      : num  1 0 0 0 0 0 0 0 0 0 ...
##  $ alt_cate      : num  0 0 0 0 0 0 1 0 0 0 ...
##  $ wbc_cate      : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ hb_cate       : num  1 0 0 0 1 0 0 1 1 0 ...
##  $ plt_cate      : num  0 1 1 0 1 1 0 0 0 1 ...
##  $ tp_cate       : num  0 1 0 0 1 0 0 1 1 0 ...
##  $ alb_cate      : num  1 0 0 0 0 0 0 1 1 1 ...
##  $ cre_cate      : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ crp_cate      : num  0 0 0 0 0 0 0 0 1 0 ...
##  $ pt_cate       : num  1 0 1 1 0 1 1 1 1 0 ...
##  $ aptt_cate     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ barthel1      : num  0 0 0 1 0 0 0 0 0 0 ...
##  $ barthel2      : num  1 1 0 0 1 0 0 0 1 1 ...

スコアリングモデル作成

# モデルのサマリーを表示
fit_reduced_model <- lrm(hosp_mortality ~ sBP_cate +gcs_cate + bil_cate + cre_cate + alb_cate+ ast_cate, data = dev_imp)
# モデルの係数を表示
coefficients(fit_reduced_model)

## Intercept  sBP_cate  gcs_cate  bil_cate  cre_cate  alb_cate  ast_cate 
## -4.318648  2.464629  1.542909  1.308507  1.210866  1.099525  1.241187

# datadistを設定
ddist <- datadist(dev_imp)
options(datadist='ddist')

# モデルのサマリーを表示
fit_reduced_model <- lrm(hosp_mortality ~ sBP_cate +gcs_cate + bil_cate + cre_cate + alb_cate+ ast_cate, data = dev_imp)
summary(fit_reduced_model)

##              Effects              Response : hosp_mortality 
## 
##  Factor      Low High Diff. Effect  S.E.    Lower 0.95 Upper 0.95
##  sBP_cate    0   1    1      2.4646 0.35250 1.77370     3.1555   
##   Odds Ratio 0   1    1     11.7590      NA 5.89290    23.4650   
##  gcs_cate    0   1    1      1.5429 0.49686 0.56909     2.5167   
##   Odds Ratio 0   1    1      4.6782      NA 1.76670    12.3880   
##  bil_cate    0   1    1      1.3085 0.48663 0.35472     2.2623   
##   Odds Ratio 0   1    1      3.7006      NA 1.42580     9.6051   
##  cre_cate    0   1    1      1.2109 0.42058 0.38654     2.0352   
##   Odds Ratio 0   1    1      3.3564      NA 1.47190     7.6537   
##  alb_cate    0   1    1      1.0995 0.35393 0.40583     1.7932   
##   Odds Ratio 0   1    1      3.0027      NA 1.50060     6.0088   
##  ast_cate    0   1    1      1.2412 0.48757 0.28557     2.1968   
##   Odds Ratio 0   1    1      3.4597      NA 1.33050     8.9962

スコアを列に加える。

dev_imp$alb_score <- ifelse(dev_imp$alb_cate == 1, 1, 0)
dev_imp$sBP_score <- ifelse(dev_imp$sBP_cate == 1, 2, 0)
dev_imp$gcs_score <- ifelse(dev_imp$gcs_cate == 1, 1, 0)
dev_imp$bil_score <- ifelse(dev_imp$bil_cate == 1, 1, 0)
dev_imp$cre_score <- ifelse(dev_imp$cre_cate == 1, 1, 0)
dev_imp$ast_score <- ifelse(dev_imp$ast_cate == 1, 1, 0)
dev_imp$sum_score <- dev_imp$alb_score + dev_imp$sBP_score + dev_imp$gcs_score + dev_imp$bil_score + dev_imp$cre_score + dev_imp$ast_score

val_imp$alb_score <- ifelse(val_imp$alb_cate == 1, 1, 0)
val_imp$sBP_score <- ifelse(val_imp$sBP_cate == 1, 2, 0)
val_imp$gcs_score <- ifelse(val_imp$gcs_cate == 1, 1, 0)
val_imp$bil_score <- ifelse(val_imp$bil_cate == 1, 1, 0)
val_imp$cre_score <- ifelse(val_imp$cre_cate == 1, 1, 0)
val_imp$ast_score <- ifelse(val_imp$ast_cate == 1, 1, 0)
val_imp$sum_score <- val_imp$alb_score + val_imp$sBP_score + val_imp$gcs_score + val_imp$bil_score + val_imp$cre_score + val_imp$ast_score

開発データのROC曲線の描図とAUC

# 開発データセットのROCカーブとAUC
roc_obj_dev <- roc(dev_imp$hosp_mortality, dev_imp$sum_score)

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

plot(roc_obj_dev, main="ROC curve for the development data")

# Calculate AUC and its confidence interval for development data
auc_roc_dev <- auc(roc_obj_dev)
ci_dev <- ci.auc(roc_obj_dev)

# Calculate the upper bound of the CI
upper_bound_dev <- 2*auc_roc_dev - ci_dev[1]

# Print the AUC and its confidence interval for development data
cat("AUC for the development data: ", auc_roc_dev, "\n")

## AUC for the development data:  0.8969496

cat("95% CI for AUC (development): (", ci_dev[1], ",", upper_bound_dev, ")\n")

## 95% CI for AUC (development): ( 0.8582525 , 0.9356467 )

検証データのROC曲線の描図とAUC

# 検証データセットのROCカーブとAUC
roc_obj_val <- roc(val_imp$hosp_mortality, val_imp$sum_score)

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

plot(roc_obj_val, main="ROC curve for the validation data")

# Calculate AUC and its confidence interval for validation data
auc_roc_val <- auc(roc_obj_val)
ci_val <- ci.auc(roc_obj_val)

# Calculate the upper bound of the CI for validation data
upper_bound_val <- 2*auc_roc_val - ci_val[1]

# Print the AUC and its confidence interval for validation data
cat("AUC for the validation data: ", auc_roc_val, "\n")

## AUC for the validation data:  0.8849067

cat("95% CI for AUC (validation): (", ci_val[1], ",", upper_bound_val, ")\n")

## 95% CI for AUC (validation): ( 0.8405372 , 0.9292762 )

開発データ・検証データを共に描図する

# 開発データと検証データのROCオブジェクトの計算
roc_obj_dev <- roc(dev_imp$hosp_mortality, dev_imp$sum_score)

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

roc_obj_val <- roc(val_imp$hosp_mortality, val_imp$sum_score)

## Setting levels: control = 0, case = 1
## Setting direction: controls < cases

# 開発データと検証データのAUCの計算
auc_dev <- auc(roc_obj_dev)
auc_val <- auc(roc_obj_val)

# ROC dataをデータフレームに変換
roc_data <- data.frame(
  Specificity = c(roc_obj_dev$specificities, roc_obj_val$specificities),
  Sensitivity = c(roc_obj_dev$sensitivities, roc_obj_val$sensitivities),
  ROC = c(rep("Development", length(roc_obj_dev$sensitivities)), rep("Validation", length(roc_obj_val$sensitivities)))
)

# Plot ROC curve
library(ggplot2)
ggplot(data = roc_data, aes(x = Specificity, y = Sensitivity, color = ROC)) +
  geom_line() +
  scale_x_reverse(limits = c(1, 0)) +
  labs(x = "Specificity", y = "Sensitivity") +
  theme_minimal() +
  scale_color_manual(values = c("Development" = "blue", "Validation" = "red")) +
  annotate("text", x = 0.75, y = 0.25, label = paste("AUC for Development = ", round(auc_dev, 3)), color="blue") +
  annotate("text", x = 0.75, y = 0.15, label = paste("AUC for Validation = ", round(auc_val, 3)), color="red")

Meld,Child score,年齢との比較開発コホート

# child_numのみを用いたモデル
model_child_num <- glm(hosp_mortality ~ child_num, data = dev_imp, family = binomial())
pred_child_num_dev <- predict(model_child_num, dev_imp, type = "response")

# ageのみを用いたモデル
model_age <- glm(hosp_mortality ~ age, data = dev_imp, family = binomial())
pred_age_dev <- predict(model_age, dev_imp, type = "response")

# MELDスコアのみを用いたモデル
model_meld <- glm(hosp_mortality ~ meld, data = dev_imp, family = binomial())
pred_meld_dev <- predict(model_meld, dev_imp, type = "response")

# ROC curveとAUCの計算
roc_obj_child_num_dev <- roc(dev_imp$hosp_mortality, pred_child_num_dev)

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

roc_obj_age_dev <- roc(dev_imp$hosp_mortality, pred_age_dev)

## Setting levels: control = 0, case = 1
## Setting direction: controls < cases

# AUCの計算
auc_child_num_dev <- auc(roc_obj_child_num_dev)
auc_age_dev <- auc(roc_obj_age_dev)

# ROC curveをデータフレームに変換
roc_data <- data.frame(
  Specificity = c(roc_obj_dev$specificities, roc_obj_child_num_dev$specificities, roc_obj_age_dev$specificities),
  Sensitivity = c(roc_obj_dev$sensitivities, roc_obj_child_num_dev$sensitivities, roc_obj_age_dev$sensitivities),
  ROC = c(rep("HOPE-EVL score", length(roc_obj_dev$sensitivities)), 
          rep("Child-Pugh score", length(roc_obj_child_num_dev$sensitivities)), 
          rep("Age", length(roc_obj_age_dev$sensitivities)))
)

# MELDスコアのみを用いたモデル
model_meld <- glm(hosp_mortality ~ meld, data = dev_imp, family = binomial())
pred_meld_dev <- predict(model_meld, dev_imp, type = "response")

# ROC curveとAUCの計算
roc_obj_meld_dev <- roc(dev_imp$hosp_mortality, pred_meld_dev)

## Setting levels: control = 0, case = 1
## Setting direction: controls < cases

# AUCの計算
auc_meld_dev <- auc(roc_obj_meld_dev)

# ROC curveをデータフレームに追加
roc_data <- rbind(roc_data, 
                  data.frame(Specificity = roc_obj_meld_dev$specificities,
                             Sensitivity = roc_obj_meld_dev$sensitivities,
                             ROC = rep("MELD score", length(roc_obj_meld_dev$sensitivities))))



# ROCカラムのファクターレベルを調整
roc_data$ROC <- factor(roc_data$ROC, levels = c("HOPE-EVL score", "MELD score", "Child-Pugh score", "Age"))

# Plot ROC curve
ggplot(data = roc_data, aes(x = Specificity, y = Sensitivity, color = ROC)) +
  geom_line() +
  scale_x_reverse(limits = c(1, 0)) +
  labs(x = "Specificity", y = "Sensitivity") +
  theme_minimal() +
  scale_color_manual(values = c("HOPE-EVL score" = "red", 
                                "MELD score" = "green3",
                                "Child-Pugh score" = "cyan",
                                "Age" = "purple3")) +
  annotate("text", x = 0.75, y = 0.25, label = paste("HOPE-EVL score = ", round(auc_dev, 3)), color="red") +
  annotate("text", x = 0.75, y = 0.20, label = paste("MELD score = ", round(auc_meld_dev, 3)), color="green3") +
  annotate("text", x = 0.75, y = 0.15, label = paste("Child-Pugh score = ", round(auc_child_num_dev, 3)), color="cyan") +
  annotate("text", x = 0.75, y = 0.10, label = paste("Age = ", round(auc_age_dev, 3)), color="purple3") 

### 開発データ各スコアでの信頼区間

# Compute CI for Child-Pugh score
ci_child_num_dev <- ci.auc(roc_obj_child_num_dev)
ci_child_num_dev_upper <- 2*auc_child_num_dev - ci_child_num_dev[1]
cat("AUC for Child-Pugh score: ", auc_child_num_dev, "\n")

## AUC for Child-Pugh score:  0.8069913

cat("95% CI for AUC (Child-Pugh score): (", ci_child_num_dev[1], ",", ci_child_num_dev_upper, ")\n")

## 95% CI for AUC (Child-Pugh score): ( 0.7525509 , 0.8614318 )

# Compute CI for Age
ci_age_dev <- ci.auc(roc_obj_age_dev)
ci_age_dev_upper <- 2*auc_age_dev - ci_age_dev[1]
cat("AUC for Age: ", auc_age_dev, "\n")

## AUC for Age:  0.5713335

cat("95% CI for AUC (Age): (", ci_age_dev[1], ",", ci_age_dev_upper, ")\n")

## 95% CI for AUC (Age): ( 0.4999139 , 0.6427531 )

# Compute CI for MELD score
ci_meld_dev <- ci.auc(roc_obj_meld_dev)
ci_meld_dev_upper <- 2*auc_meld_dev - ci_meld_dev[1]
cat("AUC for MELD score: ", auc_meld_dev, "\n")

## AUC for MELD score:  0.8087517

cat("95% CI for AUC (MELD score): (", ci_meld_dev[1], ",", ci_meld_dev_upper, ")\n")

## 95% CI for AUC (MELD score): ( 0.757591 , 0.8599124 )

Meld,Child score,年齢との比較開発コホート

# 検証データセットでの予測
pred_child_num_val <- predict(model_child_num, val_imp, type = "response")
pred_age_val <- predict(model_age, val_imp, type = "response")

# 検証データセットでのROC curveとAUCの計算
roc_obj_child_num_val <- roc(val_imp$hosp_mortality, pred_child_num_val)

## Setting levels: control = 0, case = 1

## Setting direction: controls < cases

roc_obj_age_val <- roc(val_imp$hosp_mortality, pred_age_val)

## Setting levels: control = 0, case = 1
## Setting direction: controls < cases

# 検証データセットでのAUCの計算
auc_child_num_val <- auc(roc_obj_child_num_val)
auc_age_val <- auc(roc_obj_age_val)

# ROC curveをデータフレームに変換
roc_data <- data.frame(
  Specificity = c(roc_obj_val$specificities, roc_obj_child_num_val$specificities, roc_obj_age_val$specificities),
  Sensitivity = c(roc_obj_val$sensitivities, roc_obj_child_num_val$sensitivities, roc_obj_age_val$sensitivities),
  ROC = c(rep("HOPE-EVL score", length(roc_obj_val$sensitivities)), 
          rep("Child-Pugh score", length(roc_obj_child_num_val$sensitivities)), 
          rep("Age", length(roc_obj_age_val$sensitivities)))
)

# MELDスコアのみを用いたモデルで予測
pred_meld_val <- predict(model_meld, val_imp, type = "response")
# 検証データセットでのROC curveとAUCの計算
roc_obj_meld_val <- roc(val_imp$hosp_mortality, pred_meld_val)

## Setting levels: control = 0, case = 1
## Setting direction: controls < cases

# 検証データセットでのAUCの計算
auc_meld_val <- auc(roc_obj_meld_val)
# ROC curveをデータフレームに変換
roc_data <- rbind(roc_data,
                  data.frame(Specificity = roc_obj_meld_val$specificities,
                             Sensitivity = roc_obj_meld_val$sensitivities,
                             ROC = rep("MELD score", length(roc_obj_meld_val$sensitivities))))


# ROCカラムのファクターレベルを調整
roc_data$ROC <- factor(roc_data$ROC, levels = c("HOPE-EVL score", "MELD score", "Child-Pugh score", "Age"))

# Plot ROC curve
ggplot(data = roc_data, aes(x = Specificity, y = Sensitivity, color = ROC)) +
  geom_line() +
  scale_x_reverse(limits = c(1, 0)) +
  labs(x = "Specificity", y = "Sensitivity") +
  theme_minimal() +
  scale_color_manual(values = c("HOPE-EVL score" = "red", 
                                "MELD score" = "green3",
                                "Child-Pugh score" = "cyan",
                                "Age" = "purple3")) +
  annotate("text", x = 0.75, y = 0.25, label = paste("HOPE-EVL score = ", round(auc_val, 3)), color="red") +
  annotate("text", x = 0.75, y = 0.20, label = paste("MELD score = ", round(auc_meld_val, 3)), color="green3") +
  annotate("text", x = 0.75, y = 0.15, label = paste("Child-Pugh score = ", round(auc_child_num_val, 3)), color="cyan") +
  annotate("text", x = 0.75, y = 0.10, label = paste("Age = ", round(auc_age_val, 3)), color="purple3") 

検証データ各スコアでの信頼区間

# Compute CI for Child-Pugh score
ci_child_num_val <- ci.auc(roc_obj_child_num_val)
ci_child_num_val_upper <- 2*auc_child_num_val - ci_child_num_val[1]
cat("AUC for Child-Pugh score: ", auc_child_num_val, "\n")

## AUC for Child-Pugh score:  0.797995

cat("95% CI for AUC (Child-Pugh score): (", ci_child_num_val[1], ",", ci_child_num_val_upper, ")\n")

## 95% CI for AUC (Child-Pugh score): ( 0.7269886 , 0.8690014 )

# Compute CI for Age
ci_age_val <- ci.auc(roc_obj_age_val)
ci_age_val_upper <- 2*auc_age_val - ci_age_val[1]
cat("AUC for Age: ", auc_age_val, "\n")

## AUC for Age:  0.5504595

cat("95% CI for AUC (Age): (", ci_age_val[1], ",", ci_age_val_upper, ")\n")

## 95% CI for AUC (Age): ( 0.4638899 , 0.6370291 )

# Compute CI for MELD score
ci_meld_val <- ci.auc(roc_obj_meld_val)
ci_meld_val_upper <- 2*auc_meld_val - ci_meld_val[1]
cat("AUC for MELD score: ", auc_meld_val, "\n")

## AUC for MELD score:  0.8529936

cat("95% CI for AUC (MELD score): (", ci_meld_val[1], ",", ci_meld_val_upper, ")\n")

## 95% CI for AUC (MELD score): ( 0.7942431 , 0.9117441 )

各データでのスコア合計のヒストグラム

# dev_impデータセットのsum_scoreのヒストグラム
hist(dev_imp$sum_score, main = "Histogram of total score for development data", xlab = "sum_score")

# val_impデータセットのsum_scoreのヒストグラム
hist(val_imp$sum_score, main = "Histogram of total score for validation data", xlab = "sum_score")

## calibration

スコアリング前のモデルでのcalibration評価

まずはモデル式を定義

#モデルの構築
dev_imp <-
  dev_imp|> 　
  mutate(hosp_mortality=as.numeric(hosp_mortality)) #numericにしないと回帰が回らない
val_imp <- val_imp |>
  mutate(hosp_mortality = as.numeric(hosp_mortality)-1) #valは1,2になってしまっているため対処

model <- glm(hosp_mortality ~ sBP_cate +gcs_cate + bil_cate + cre_cate + alb_cate+ ast_cate, data = dev_imp, family = binomial())

# サマリー統計量の取得
summary_stats <- summary(model)

# 係数(coefficient)、標準誤差(SE)、およびp値の取得
coefficients <- summary_stats$coefficients

# 各項目の結果を表示
print(coefficients)

##              Estimate Std. Error    z value
## (Intercept) -4.318648  0.3819340 -11.307315
## sBP_cate     2.464629  0.3524959   6.991938
## gcs_cate     1.542909  0.4968547   3.105352
## bil_cate     1.308507  0.4866315   2.688907
## cre_cate     1.210866  0.4205815   2.879029
## alb_cate     1.099525  0.3539299   3.106618
## ast_cate     1.241187  0.4875679   2.545671
##                                          Pr(>|z|)
## (Intercept) 0.00000000000000000000000000001207276
## sBP_cate    0.00000000000271114941492946932195868
## gcs_cate    0.00190052604539989351828399577470918
## bil_cate    0.00716864044447794966641307468080413
## cre_cate    0.00398902194148657591316053938612640
## alb_cate    0.00189241065622248907755753588588732
## ast_cate    0.01090680698289481741214412835461189

# 変数リストの設定（応答変数を除外）
initial_vars_dammy <- c("sex","barthel1","barthel2", "pept_ulcer", "dm", "malignancy", "hcc", "alcohol", 
                  "past_rupture", "antithro", "steroid", "beta", "sBP_cate", 
                  "age_cate", "bmi_cate", "gcs_cate", "bt_cate", "bil_cate", "ast_cate", "alt_cate", 
                  "wbc_cate", "hb_cate", "plt_cate", "alb_cate", "cre_cate", 
                  "crp_cate", "aptt_cate","pt_cate" )

# Initialize an empty data frame for results
results <- data.frame()

# Loop through each variable in the list
for (var in initial_vars_dammy) {
  
  # Perform logistic regression
  model_crude <- glm(formula(paste("hosp_mortality ~", var)), data = dev_imp, family = "binomial")
  
  # Calculate odds ratio, 95% CI, and p-value
  coefs <- coef(summary(model_crude))
  OR <- round(exp(coefs[2, "Estimate"]), 2)
  lower <- round(exp(coefs[2, "Estimate"] - 1.96 * coefs[2, "Std. Error"]), 2)
  upper <- round(exp(coefs[2, "Estimate"] + 1.96 * coefs[2, "Std. Error"]), 2)
  p_value <- round(coefs[2, "Pr(>|z|)"], 3)
  
  # Add results to the data frame
  results <- rbind(results, data.frame(Variable = var, OR = OR, Lower = lower, Upper = upper, P_value = p_value))
}

# Show the results
print(results)

##        Variable    OR Lower  Upper P_value
## 1           sex  1.20  0.69   2.09   0.527
## 2      barthel1  0.44  0.23   0.85   0.015
## 3      barthel2  6.91  3.94  12.09   0.000
## 4    pept_ulcer  0.10  0.01   0.71   0.022
## 5            dm  0.46  0.21   0.99   0.046
## 6    malignancy  1.78  0.89   3.55   0.100
## 7           hcc  1.09  0.60   1.98   0.784
## 8       alcohol  0.70  0.42   1.16   0.166
## 9  past_rupture  0.47  0.22   1.01   0.054
## 10     antithro  2.13  0.22  20.75   0.515
## 11      steroid  6.42  0.40 103.74   0.190
## 12         beta  0.00  0.00    Inf   0.979
## 13     sBP_cate 16.26  8.97  29.47   0.000
## 14     age_cate  1.20  0.72   1.98   0.488
## 15     bmi_cate  0.57  0.28   1.15   0.116
## 16     gcs_cate 16.59  7.58  36.34   0.000
## 17      bt_cate  0.71  0.39   1.28   0.258
## 18     bil_cate  5.81  2.93  11.53   0.000
## 19     ast_cate  3.99  1.97   8.07   0.000
## 20     alt_cate  2.67  1.23   5.79   0.013
## 21     wbc_cate  1.50  0.82   2.76   0.192
## 22      hb_cate  1.44  0.88   2.36   0.150
## 23     plt_cate  1.04  0.64   1.71   0.863
## 24     alb_cate  6.36  3.50  11.57   0.000
## 25     cre_cate  6.00  3.33  10.80   0.000
## 26     crp_cate  4.38  2.27   8.45   0.000
## 27    aptt_cate 10.93  4.48  26.66   0.000
## 28      pt_cate  2.38  1.42   3.97   0.001

開発データ,モデル式でのcalibrationのための割合比較

# 予測確率の計算
pred_dev <- predict(model, data = dev_imp, type = "response")
dev_imp$predicted <- pred_dev

# スコアごとに要約
dev_by <- dplyr::group_by(dev_imp, sum_score)
table_dev <- dplyr::summarize(dev_by,
      hosp_mortality = sum(hosp_mortality),　# outcomeは１/0
      n = n(),
      mortality_rate = round(hosp_mortality / n, 3),
      prediction = round(mean(predicted), 3)
    )
table_dev

## # A tibble: 8 × 5
##   sum_score hosp_mortality     n mortality_rate prediction
##       <dbl>          <dbl> <int>          <dbl>      <dbl>
## 1         0              2   215          0.009      0.013
## 2         1              7   149          0.047      0.04 
## 3         2              7    61          0.115      0.133
## 4         3             23    63          0.365      0.332
## 5         4             20    32          0.625      0.645
## 6         5              8    10          0.8        0.874
## 7         6              5     5          1          0.962
## 8         7              1     1          1          0.99

検証データ,モデル式でのcalibrationのための割合比較

# 予測確率の計算
pred_val <- predict(model, newdata = val_imp, type = "response")
val_imp$predicted <- pred_val


# スコアごとに要約
val_by <- dplyr::group_by(val_imp, sum_score)
table_val <- dplyr::summarize(val_by,
      hosp_mortality = sum(hosp_mortality),　# outcomeは１/0
      n = n(),
      mortality_rate = round(hosp_mortality / n, 3),
      prediction = round(mean(predicted), 3)
    )
table_val

## # A tibble: 7 × 5
##   sum_score hosp_mortality     n mortality_rate prediction
##       <dbl>          <dbl> <int>          <dbl>      <dbl>
## 1         0              0   187          0          0.013
## 2         1              8   119          0.067      0.041
## 3         2              5    59          0.085      0.131
## 4         3             11    46          0.239      0.33 
## 5         4             14    22          0.636      0.641
## 6         5              4     8          0.5        0.852
## 7         6              3     3          1          0.953

開発データでの診断精度

# For each cut-off point, calculate the metrics
for (cut_off in 0:7) {
  # Create a binary variable to separate the group from the others
  dev_imp$group_binary_dev <- ifelse(dev_imp$sum_score >= cut_off, 1, 0)
  
  # Calculate confusion matrix
  CM_dev <- table(Predicted = dev_imp$group_binary_dev, Actual = dev_imp$hosp_mortality)
  
  # If confusion matrix is not 2x2, adjust it
  if (any(dim(CM_dev) != c(2, 2))) {
    levels_factor_dev <- levels(as.factor(dev_imp$hosp_mortality))
    CM_dev <- matrix(c(CM_dev, rep(0, 4 - length(CM_dev))), nrow = 2,
                 dimnames = list(Predicted = c(0, 1), Actual = levels_factor_dev))
  }

  # Values from confusion matrix
  TP_dev <- ifelse(is.na(CM_dev[2, 2]), 0, CM_dev[2, 2])
  FP_dev <- ifelse(is.na(CM_dev[2, 1]), 0, CM_dev[2, 1])
  TN_dev <- ifelse(is.na(CM_dev[1, 1]), 0, CM_dev[1, 1])
  FN_dev <- ifelse(is.na(CM_dev[1, 2]), 0, CM_dev[1, 2])

  # Calculate metrics
  if ((FP_dev + TN_dev) != 0) {Sp_dev <- TN_dev / (FP_dev + TN_dev)} else {Sp_dev <- NA}
  if ((TP_dev + FN_dev) != 0) {Se_dev <- TP_dev / (TP_dev + FN_dev)} else {Se_dev <- NA}
  if ((1 - Sp_dev) != 0) {LR_plus_dev <- Se_dev / (1 - Sp_dev)} else {LR_plus_dev <- NA}
  if (Sp_dev != 0) {LR_minus_dev <- (1 - Se_dev) / Sp_dev} else {LR_minus_dev <- NA}

  # Print results
  cat("Cut-off: ≥", cut_off, "\n")
  print(CM_dev) # Added here to print the confusion matrix
  cat("特異度（Sp）: ", Sp_dev, "\n")
  cat("感度（Se）: ", Se_dev, "\n")
  cat("陽性尤度比（LR+）: ", LR_plus_dev, "\n")
  cat("陰性尤度比（LR-）: ", LR_minus_dev, "\n")
  cat("真陽性数（TP）: ", TP_dev, "\n")
  cat("真陰性数（TN）: ", TN_dev, "\n")
  cat("偽陽性数（FP）: ", FP_dev, "\n")
  cat("偽陰性数（FN）: ", FN_dev, "\n\n")
}

## Cut-off: ≥ 0 
##          Actual
## Predicted   0 1
##         0 463 0
##         1  73 0
## 特異度（Sp）:  0.863806 
## 感度（Se）:  NA 
## 陽性尤度比（LR+）:  NA 
## 陰性尤度比（LR-）:  NA 
## 真陽性数（TP）:  0 
## 真陰性数（TN）:  463 
## 偽陽性数（FP）:  73 
## 偽陰性数（FN）:  0 
## 
## Cut-off: ≥ 1 
##          Actual
## Predicted   0   1
##         0 213   2
##         1 250  71
## 特異度（Sp）:  0.4600432 
## 感度（Se）:  0.9726027 
## 陽性尤度比（LR+）:  1.80126 
## 陰性尤度比（LR-）:  0.05955367 
## 真陽性数（TP）:  71 
## 真陰性数（TN）:  213 
## 偽陽性数（FP）:  250 
## 偽陰性数（FN）:  2 
## 
## Cut-off: ≥ 2 
##          Actual
## Predicted   0   1
##         0 355   9
##         1 108  64
## 特異度（Sp）:  0.7667387 
## 感度（Se）:  0.8767123 
## 陽性尤度比（LR+）:  3.758498 
## 陰性尤度比（LR-）:  0.1607949 
## 真陽性数（TP）:  64 
## 真陰性数（TN）:  355 
## 偽陽性数（FP）:  108 
## 偽陰性数（FN）:  9 
## 
## Cut-off: ≥ 3 
##          Actual
## Predicted   0   1
##         0 409  16
##         1  54  57
## 特異度（Sp）:  0.8833693 
## 感度（Se）:  0.7808219 
## 陽性尤度比（LR+）:  6.694825 
## 陰性尤度比（LR-）:  0.248116 
## 真陽性数（TP）:  57 
## 真陰性数（TN）:  409 
## 偽陽性数（FP）:  54 
## 偽陰性数（FN）:  16 
## 
## Cut-off: ≥ 4 
##          Actual
## Predicted   0   1
##         0 449  39
##         1  14  34
## 特異度（Sp）:  0.9697624 
## 感度（Se）:  0.4657534 
## 陽性尤度比（LR+）:  15.40313 
## 陰性尤度比（LR-）:  0.5509046 
## 真陽性数（TP）:  34 
## 真陰性数（TN）:  449 
## 偽陽性数（FP）:  14 
## 偽陰性数（FN）:  39 
## 
## Cut-off: ≥ 5 
##          Actual
## Predicted   0   1
##         0 461  59
##         1   2  14
## 特異度（Sp）:  0.9956803 
## 感度（Se）:  0.1917808 
## 陽性尤度比（LR+）:  44.39726 
## 陰性尤度比（LR-）:  0.8117256 
## 真陽性数（TP）:  14 
## 真陰性数（TN）:  461 
## 偽陽性数（FP）:  2 
## 偽陰性数（FN）:  59 
## 
## Cut-off: ≥ 6 
##          Actual
## Predicted   0   1
##         0 463  67
##         1   0   6
## 特異度（Sp）:  1 
## 感度（Se）:  0.08219178 
## 陽性尤度比（LR+）:  NA 
## 陰性尤度比（LR-）:  0.9178082 
## 真陽性数（TP）:  6 
## 真陰性数（TN）:  463 
## 偽陽性数（FP）:  0 
## 偽陰性数（FN）:  67 
## 
## Cut-off: ≥ 7 
##          Actual
## Predicted   0   1
##         0 463  72
##         1   0   1
## 特異度（Sp）:  1 
## 感度（Se）:  0.01369863 
## 陽性尤度比（LR+）:  NA 
## 陰性尤度比（LR-）:  0.9863014 
## 真陽性数（TP）:  1 
## 真陰性数（TN）:  463 
## 偽陽性数（FP）:  0 
## 偽陰性数（FN）:  72

#### 注意 cut-off≥0のみ誤って記載される。真陽性：73,偽陽性：463が正しい。感度・特異度・尤度比の数値は注意

開発データ、モデル式でのグラフ化

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
dev_imp <- dev_imp %>%
  mutate(sum_score_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "0 to 1",
    sum_score >= 2 & sum_score <= 3 ~ "2 to 3",
    sum_score == 4 ~ "4",
    sum_score == 5 ~ "5",
    sum_score == 6 ~ "6",
    sum_score == 7 ~ "7"
  ))

# 'sum_score_group'を順序付きの因子として扱う
dev_imp$sum_score_group <- factor(dev_imp$sum_score_group, levels = c("0 to 1", "2 to 3", "4", "5", "6", "7"), ordered = TRUE)

# 'predicted'列の名前を'prediction'に変更します
names(dev_imp)[names(dev_imp) == "predicted"] <- "prediction"

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table <- dev_imp %>%
  group_by(sum_score_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), prediction = mean(prediction, na.rm = TRUE)) %>%
  mutate(mortality_rate = hosp_mortality / n)

# pivot_longerを使って長い形式に変換
summary_table_long <- summary_table %>%
  pivot_longer(c(mortality_rate, prediction), names_to = "variable", values_to = "value")

# 新しい形式で棒グラフをプロット
ggplot(summary_table_long, aes(x = sum_score_group, y = value*100, fill = variable)) +
  geom_col(position = "dodge", width = 0.6) +
  labs(y = "Percentage (%)", x = "Sum Score Group", fill = "Variable") +
  scale_fill_manual(values = c("mortality_rate" = "blue", "prediction" = "red")) +
  theme_minimal()

検証データ、モデル式でのグラフ化

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
val_imp <- val_imp %>%
  mutate(sum_score_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "0 to 1",
    sum_score >= 2 & sum_score <= 3 ~ "2 to 3",
    sum_score == 4 ~ "4",
    sum_score == 5 ~ "5",
    sum_score == 6 ~ "6",
    sum_score == 7 ~ "7"
  ))

# 'sum_score_group'を順序付きの因子として扱う
val_imp$sum_score_group <- factor(val_imp$sum_score_group, levels = c("0 to 1", "2 to 3", "4", "5", "6", "7"), ordered = TRUE)

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_val <- val_imp %>%
  group_by(sum_score_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), prediction = mean(predicted)) %>%
  mutate(mortality_rate = hosp_mortality / n)

# データを長い形式に変換します
summary_table_long_val <- summary_table_val %>%
  pivot_longer(c(mortality_rate, prediction), names_to = "variable", values_to = "value")

# 棒グラフをプロットします
ggplot(summary_table_long_val, aes(x = sum_score_group, y = value*100, fill = variable)) +
  geom_col(position = "dodge", width = 0.6) +
  labs(y = "Percentage (%)", x = "Sum Score Group", fill = "Variable") +
  scale_fill_manual(values = c("mortality_rate" = "blue", "prediction" = "red")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

スコアモデル 開発データでのcaliration評価

# 手動で予測値を計算します
liner_predict_manual <- -4.318648 + 2.5 * dev_imp$sBP_cate + 1.25 * dev_imp$gcs_cate + 1.25 * dev_imp$bil_cate + 1.25 * dev_imp$cre_cate + 1.25 * dev_imp$alb_cate  + 1.25 * dev_imp$ast_cate 
pred_manual <- exp(liner_predict_manual) / (exp(liner_predict_manual) + 1)

# 新しい予測値でテーブルを作成します
dev_imp$predicted_manual <- pred_manual
dev_by_manual <- dplyr::group_by(dev_imp, sum_score)
table_dev_manual <- dplyr::summarize(dev_by_manual,
      hosp_mortality = sum(hosp_mortality),
      n = n(),
      mortality_rate = round(hosp_mortality / n, 3),
      prediction_manual = round(mean(predicted_manual), 3)
    )
table_dev_manual

## # A tibble: 8 × 5
##   sum_score hosp_mortality     n mortality_rate prediction_manual
##       <dbl>          <dbl> <int>          <dbl>             <dbl>
## 1         0              2   215          0.009             0.013
## 2         1              7   149          0.047             0.044
## 3         2              7    61          0.115             0.14 
## 4         3             23    63          0.365             0.362
## 5         4             20    32          0.625             0.664
## 6         5              8    10          0.8               0.873
## 7         6              5     5          1                 0.96 
## 8         7              1     1          1                 0.988

スコアモデル 開発データでのcaliration グラフ化

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
dev_imp <- dev_imp %>%
  mutate(sum_score_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "0 to 1",
    sum_score >= 2 & sum_score <= 3 ~ "2 to 3",
    sum_score == 4 ~ "4",
    sum_score == 5 ~ "5",
    sum_score == 6 ~ "6",
    sum_score == 7 ~ "7"
  ))

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_dev_manual <- dev_imp %>%
  group_by(sum_score_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), Prediction = mean(predicted_manual)) %>%
  mutate(Observed = hosp_mortality / n)

# データを長い形式に変換します
summary_table_long_dev_manual <- summary_table_dev_manual %>%
  pivot_longer(c(Observed, Prediction), names_to = "variable", values_to = "value")

# 棒グラフをプロットします
ggplot(summary_table_long_dev_manual, aes(x = sum_score_group, y = value*100, fill = variable)) +
  geom_col(position = "dodge", width = 0.6) +
  labs(y = "Percentage (%)", x = "Sum Score Group", fill = "Variable") +
  scale_fill_manual(values = c("Observed" = "blue", "Prediction" = "red")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

開発データスコアで4,5点をまとめてみる very highあり

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
dev_imp <- dev_imp %>%
  mutate(sum_score_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "Low (Score 0,1)",
    sum_score >= 2 & sum_score <= 3 ~ "Middle (Score 2,3)",
    sum_score >= 4 & sum_score <= 5 ~ "High (Score 4,5)",
    sum_score >= 6 & sum_score <= 7 ~ "Very high (Score 6,7)"
  ))

# 因子として定義し、レベルを指定します
dev_imp$sum_score_group <- factor(dev_imp$sum_score_group, 
                                  levels = c("Low (Score 0,1)", "Middle (Score 2,3)", 
                                             "High (Score 4,5)", "Very high (Score 6,7)"))

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_dev_manual <- dev_imp %>%
  group_by(sum_score_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), Prediction = mean(predicted_manual)) %>%
  mutate(Observed = hosp_mortality / n)

# データを長い形式に変換します
summary_table_long_dev_manual <- summary_table_dev_manual %>%
  pivot_longer(c(Observed, Prediction), names_to = "variable", values_to = "value")

# 棒グラフをプロットします
ggplot(summary_table_long_dev_manual, aes(x = sum_score_group, y = value*100, fill = variable)) +
  geom_col(position = "dodge", width = 0.6) +
  labs(y = "Percentage (%)", x = "Group", fill = "Variable") +
  scale_fill_manual(values = c("Observed" = "blue", "Prediction" = "red")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

開発データスコアで4-6点をhighとしてまとめてみる very highなしのプロット

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
dev_imp <- dev_imp %>%
  mutate(sum_score_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "Low (Score 0,1)",
    sum_score >= 2 & sum_score <= 3 ~ "Middle (Score 2,3)",
    sum_score >= 4 & sum_score <= 7 ~ "High (Score ≥4)"
  ))

# 因子として定義し、レベルを指定します
dev_imp$sum_score_group <- factor(dev_imp$sum_score_group, 
                                  levels = c("Low (Score 0,1)", "Middle (Score 2,3)", 
                                             "High (Score ≥4)"))

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_dev_manual <- dev_imp %>%
  group_by(sum_score_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), Prediction = mean(predicted_manual)) %>%
  mutate(Observed = hosp_mortality / n)

# データを長い形式に変換します
summary_table_long_dev_manual <- summary_table_dev_manual %>%
  pivot_longer(c(Observed, Prediction), names_to = "variable", values_to = "value")

# 棒グラフをプロットします
ggplot(summary_table_long_dev_manual, aes(x = sum_score_group, y = value*100, fill = variable)) +
  geom_col(position = "dodge", width = 0.6) +
  labs(y = "Percentage (%)", x = "Group", fill = "Variable") +
  scale_fill_manual(values = c("Observed" = "blue", "Prediction" = "red")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

開発データリスク別の予想と実測を算出 Very highあり

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
dev_imp <- dev_imp %>%
  mutate(risk_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "Low risk",
    sum_score >= 2 & sum_score <= 3 ~ "Middle risk",
    sum_score >= 4 & sum_score <= 5 ~ "High risk",
    sum_score >= 6 & sum_score <= 7 ~ "Very high (Score 6,7)"
  ))

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_dev_manual <- dev_imp %>%
  group_by(risk_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), Prediction = mean(predicted_manual)) %>%
  mutate(Observed = hosp_mortality / n)

# 各リスクグループのObservedとPredictionの具体的な数値を表示します
summary_table_dev_manual %>% select(risk_group, Observed, Prediction)

## # A tibble: 4 × 3
##   risk_group            Observed Prediction
##   <chr>                    <dbl>      <dbl>
## 1 High risk               0.667      0.714 
## 2 Low risk                0.0247     0.0259
## 3 Middle risk             0.242      0.252 
## 4 Very high (Score 6,7)   1          0.965

very highなし　開発データリスク別の予想と実測を算出

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
dev_imp <- dev_imp %>%
  mutate(risk_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "Low risk",
    sum_score >= 2 & sum_score <= 3 ~ "Middle risk",
    sum_score >= 4 & sum_score <= 7 ~ "High risk"
  ))

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_dev_manual <- dev_imp %>%
  group_by(risk_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), Prediction = mean(predicted_manual)) %>%
  mutate(Observed = hosp_mortality / n)

# 各リスクグループのObservedとPredictionの具体的な数値を表示します
summary_table_dev_manual %>% select(risk_group, Observed, Prediction)

## # A tibble: 3 × 3
##   risk_group  Observed Prediction
##   <chr>          <dbl>      <dbl>
## 1 High risk     0.708      0.745 
## 2 Low risk      0.0247     0.0259
## 3 Middle risk   0.242      0.252

# Load necessary library
library(Hmisc)

# Add standard error and confidence intervals
summary_table_dev_manual <- summary_table_dev_manual %>%
  group_by(risk_group) %>%
  mutate(
    se = sqrt((Prediction * (1 - Prediction)) / n),
    ci_lower = Prediction - qt(1 - (0.05 / 2), df = n - 1) * se,
    ci_upper = Prediction + qt(1 - (0.05 / 2), df = n - 1) * se
  )

summary_table_dev_manual

## # A tibble: 3 × 8
## # Groups:   risk_group [3]
##   risk_group  hosp_mortality     n Prediction Observed      se ci_lower ci_upper
##   <chr>                <dbl> <int>      <dbl>    <dbl>   <dbl>    <dbl>    <dbl>
## 1 High risk               34    48     0.745    0.708  0.0629   0.619     0.872 
## 2 Low risk                 9   364     0.0259   0.0247 0.00833  0.00956   0.0423
## 3 Middle risk             30   124     0.252    0.242  0.0390   0.175     0.330

スコアモデル 検証データでのcaliration評価

# 手動で予測値を計算します
liner_predict_manual_val <- -4.318648 + 2.5 * val_imp$sBP_cate + 1.25 * val_imp$gcs_cate + 1.25 * val_imp$bil_cate + 1.25 * val_imp$cre_cate + 1.25 * val_imp$alb_cate + 1.25 * val_imp$ast_cate 
pred_manual_val <- exp(liner_predict_manual_val) / (exp(liner_predict_manual_val) + 1)

# 新しい予測値でテーブルを作成します
val_imp$predicted_manual <- pred_manual_val
val_by_manual <- dplyr::group_by(val_imp, sum_score)
table_val_manual <- dplyr::summarize(val_by_manual,
      hosp_mortality = sum(hosp_mortality),
      n = n(),
      mortality_rate = round(hosp_mortality / n, 3),
      prediction_manual = round(mean(predicted_manual), 3)
    )
table_val_manual

## # A tibble: 7 × 5
##   sum_score hosp_mortality     n mortality_rate prediction_manual
##       <dbl>          <dbl> <int>          <dbl>             <dbl>
## 1         0              0   187          0                 0.013
## 2         1              8   119          0.067             0.044
## 3         2              5    59          0.085             0.14 
## 4         3             11    46          0.239             0.362
## 5         4             14    22          0.636             0.664
## 6         5              4     8          0.5               0.873
## 7         6              3     3          1                 0.96

スコアモデル 検証データでのcaliration グラフ化

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
val_imp <- val_imp %>%
  mutate(sum_score_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "0 to 1",
    sum_score >= 2 & sum_score <= 3 ~ "2 to 3",
    sum_score == 4 ~ "4",
    sum_score == 5 ~ "5",
    sum_score == 6 ~ "6",
    sum_score == 7 ~ "7"
  ))

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_val_manual <- val_imp %>%
  group_by(sum_score_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), Prediction = mean(predicted_manual)) %>%
  mutate(Observed = hosp_mortality / n)

# データを長い形式に変換します
summary_table_long_val_manual <- summary_table_val_manual %>%
  pivot_longer(c(Observed, Prediction), names_to = "variable", values_to = "value")

# 棒グラフをプロットします
ggplot(summary_table_long_val_manual, aes(x = sum_score_group, y = value*100, fill = variable)) +
  geom_col(position = "dodge", width = 0.6) +
  labs(y = "Percentage (%)", x = "Sum Score Group", fill = "Variable") +
  scale_fill_manual(values = c("Observed" = "blue", "Prediction" = "red")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

検証4,5点をまとめてみる

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
val_imp <- val_imp %>%
  mutate(sum_score_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "Low (Score 0,1)",
    sum_score >= 2 & sum_score <= 3 ~ "Middle (Score 2,3)",
    sum_score >= 4 & sum_score <= 5 ~ "High (Score 4,5)",
    sum_score >= 6 & sum_score <= 7 ~ "Very high (Score 6,7)"
  ))

# 因子として定義し、レベルを指定します
val_imp$sum_score_group <- factor(val_imp$sum_score_group, 
                                  levels = c("Low (Score 0,1)", "Middle (Score 2,3)", 
                                             "High (Score 4,5)", "Very high (Score 6,7)"))

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_val_manual <- val_imp %>%
  group_by(sum_score_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), Prediction = mean(predicted_manual)) %>%
  mutate(Observed = hosp_mortality / n)

# データを長い形式に変換します
summary_table_long_val_manual <- summary_table_val_manual %>%
  pivot_longer(c(Observed, Prediction), names_to = "variable", values_to = "value")

# 棒グラフをプロットします
ggplot(summary_table_long_val_manual, aes(x = sum_score_group, y = value*100, fill = variable)) +
  geom_col(position = "dodge", width = 0.6) +
  labs(y = "Percentage (%)", x = "Group", fill = "Variable") +
  scale_fill_manual(values = c("Observed" = "blue", "Prediction" = "red")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

検証コホート4-6をhighとしvery highなし

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
val_imp <- val_imp %>%
  mutate(sum_score_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "Low risk (Score 0,1)",
    sum_score >= 2 & sum_score <= 3 ~ "Middle risk (Score 2,3)",
    sum_score >= 4 & sum_score <= 7 ~ "High risk (Score ≥4)"
  ))

# 因子として定義し、レベルを指定します
val_imp$sum_score_group <- factor(val_imp$sum_score_group, 
                                  levels = c("Low risk (Score 0,1)", "Middle risk (Score 2,3)", 
                                             "High risk (Score ≥4)"))

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_val_manual <- val_imp %>%
  group_by(sum_score_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), Prediction = mean(predicted_manual)) %>%
  mutate(Observed = hosp_mortality / n)

# データを長い形式に変換します
summary_table_long_val_manual <- summary_table_val_manual %>%
  pivot_longer(c(Observed, Prediction), names_to = "variable", values_to = "value")

# 棒グラフをプロットします
ggplot(summary_table_long_val_manual, aes(x = sum_score_group, y = value*100, fill = variable)) +
  geom_col(position = "dodge", width = 0.6) +
  labs(y = "Percentage (%)", x = "Group", fill = "Variable") +
  scale_fill_manual(values = c("Observed" = "blue", "Prediction" = "red")) +
  theme_minimal() +
  theme(plot.title = element_text(hjust = 0.5))

検証データリスク別の予想と実測を算出very highあり

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
val_imp <- val_imp %>%
  mutate(risk_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "Low risk (0,1)",
    sum_score >= 2 & sum_score <= 3 ~ "Middle risk (2,3)",
    sum_score >= 4 & sum_score <= 5 ~ "High risk (4,5)",
    sum_score >= 6 & sum_score <= 7 ~ "Very high risk (6,7)"
  ))

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_val_manual <- val_imp %>%
  group_by(risk_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), Prediction = mean(predicted_manual)) %>%
  mutate(Observed = hosp_mortality / n)

# 各リスクグループのObservedとPredictionの具体的な数値を表示します
summary_table_val_manual %>% select(risk_group, Observed, Prediction)

## # A tibble: 4 × 3
##   risk_group           Observed Prediction
##   <chr>                   <dbl>      <dbl>
## 1 High risk (4,5)        0.6        0.720 
## 2 Low risk (0,1)         0.0261     0.0253
## 3 Middle risk (2,3)      0.152      0.237 
## 4 Very high risk (6,7)   1          0.960

検証データリスク別の予想と実測を算出 4-6点でひとまとめ　very highなし。

# 'sum_score' の範囲に基づいて新しいカテゴリ列を作成します
val_imp <- val_imp %>%
  mutate(risk_group = case_when(
    sum_score >= 0 & sum_score <= 1 ~ "Low risk (0,1)",
    sum_score >= 2 & sum_score <= 3 ~ "Middle risk (2,3)",
    sum_score >= 4 & sum_score <= 7 ~ "High risk (4-7)"
  ))

# グループごとの 'hosp_mortality' の合計と 'n' の合計を計算します
summary_table_val_manual <- val_imp %>%
  group_by(risk_group) %>%
  summarise(hosp_mortality = sum(hosp_mortality), n = n(), Prediction = mean(predicted_manual)) %>%
  mutate(Observed = hosp_mortality / n)

# 各リスクグループのObservedとPredictionの具体的な数値を表示します
summary_table_val_manual %>% select(risk_group, Observed, Prediction)

## # A tibble: 3 × 3
##   risk_group        Observed Prediction
##   <chr>                <dbl>      <dbl>
## 1 High risk (4-7)     0.636      0.742 
## 2 Low risk (0,1)      0.0261     0.0253
## 3 Middle risk (2,3)   0.152      0.237

# Load necessary library
library(Hmisc)

# Add standard error and confidence intervals
summary_table_val_manual <- summary_table_val_manual %>%
  group_by(risk_group) %>%
  mutate(
    se = sqrt((Prediction * (1 - Prediction)) / n),
    ci_lower = Prediction - qt(1 - (0.05 / 2), df = n - 1) * se,
    ci_upper = Prediction + qt(1 - (0.05 / 2), df = n - 1) * se
  )

summary_table_val_manual

## # A tibble: 3 × 8
## # Groups:   risk_group [3]
##   risk_group  hosp_mortality     n Prediction Observed      se ci_lower ci_upper
##   <chr>                <dbl> <int>      <dbl>    <dbl>   <dbl>    <dbl>    <dbl>
## 1 High risk …             21    33     0.742    0.636  0.0762   0.587     0.897 
## 2 Low risk (…              8   306     0.0253   0.0261 0.00898  0.00764   0.0430
## 3 Middle ris…             16   105     0.237    0.152  0.0415   0.155     0.319

検証コホートでの感度・特異度・陽性尤度比・陰性尤度比・真陽性・真陰性・偽陽性・偽陰性の算出

各スコアで診断精度を確認

# For each cut-off point, calculate the metrics
for (cut_off in 0:6) {
  # Create a binary variable to separate the group from the others
  val_imp$group_binary <- ifelse(val_imp$sum_score >= cut_off, 1, 0)
  
  # Calculate confusion matrix
  CM <- table(Predicted = val_imp$group_binary, Actual = val_imp$hosp_mortality)
  
  # If confusion matrix is not 2x2, adjust it
  if (any(dim(CM) != c(2, 2))) {
    levels_factor <- levels(as.factor(val_imp$hosp_mortality))
    CM <- matrix(c(CM, rep(0, 4 - length(CM))), nrow = 2,
                 dimnames = list(Predicted = c(0, 1), Actual = levels_factor))
  }

  # Values from confusion matrix
  TP <- ifelse(is.na(CM[2, 2]), 0, CM[2, 2])
  FP <- ifelse(is.na(CM[2, 1]), 0, CM[2, 1])
  TN <- ifelse(is.na(CM[1, 1]), 0, CM[1, 1])
  FN <- ifelse(is.na(CM[1, 2]), 0, CM[1, 2])

  # Calculate metrics
  if ((FP + TN) != 0) {Sp <- TN / (FP + TN)} else {Sp <- NA}
  if ((TP + FN) != 0) {Se <- TP / (TP + FN)} else {Se <- NA}
  if ((1 - Sp) != 0) {LR_plus <- Se / (1 - Sp)} else {LR_plus <- NA}
  if (Sp != 0) {LR_minus <- (1 - Se) / Sp} else {LR_minus <- NA}

  # Print results
  cat("Cut-off: ≥", cut_off, "\n")
  print(CM) # Added here to print the confusion matrix
  cat("特異度（Sp）: ", Sp, "\n")
  cat("感度（Se）: ", Se, "\n")
  cat("陽性尤度比（LR+）: ", LR_plus, "\n")
  cat("陰性尤度比（LR-）: ", LR_minus, "\n")
  cat("真陽性数（TP）: ", TP, "\n")
  cat("真陰性数（TN）: ", TN, "\n")
  cat("偽陽性数（FP）: ", FP, "\n")
  cat("偽陰性数（FN）: ", FN, "\n\n")
}

## Cut-off: ≥ 0 
##          Actual
## Predicted   0 1
##         0 399 0
##         1  45 0
## 特異度（Sp）:  0.8986486 
## 感度（Se）:  NA 
## 陽性尤度比（LR+）:  NA 
## 陰性尤度比（LR-）:  NA 
## 真陽性数（TP）:  0 
## 真陰性数（TN）:  399 
## 偽陽性数（FP）:  45 
## 偽陰性数（FN）:  0 
## 
## Cut-off: ≥ 1 
##          Actual
## Predicted   0   1
##         0 187   0
##         1 212  45
## 特異度（Sp）:  0.4686717 
## 感度（Se）:  1 
## 陽性尤度比（LR+）:  1.882075 
## 陰性尤度比（LR-）:  0 
## 真陽性数（TP）:  45 
## 真陰性数（TN）:  187 
## 偽陽性数（FP）:  212 
## 偽陰性数（FN）:  0 
## 
## Cut-off: ≥ 2 
##          Actual
## Predicted   0   1
##         0 298   8
##         1 101  37
## 特異度（Sp）:  0.7468672 
## 感度（Se）:  0.8222222 
## 陽性尤度比（LR+）:  3.248185 
## 陰性尤度比（LR-）:  0.2380313 
## 真陽性数（TP）:  37 
## 真陰性数（TN）:  298 
## 偽陽性数（FP）:  101 
## 偽陰性数（FN）:  8 
## 
## Cut-off: ≥ 3 
##          Actual
## Predicted   0   1
##         0 352  13
##         1  47  32
## 特異度（Sp）:  0.8822055 
## 感度（Se）:  0.7111111 
## 陽性尤度比（LR+）:  6.036879 
## 陰性尤度比（LR-）:  0.3274621 
## 真陽性数（TP）:  32 
## 真陰性数（TN）:  352 
## 偽陽性数（FP）:  47 
## 偽陰性数（FN）:  13 
## 
## Cut-off: ≥ 4 
##          Actual
## Predicted   0   1
##         0 387  24
##         1  12  21
## 特異度（Sp）:  0.9699248 
## 感度（Se）:  0.4666667 
## 陽性尤度比（LR+）:  15.51667 
## 陰性尤度比（LR-）:  0.5498708 
## 真陽性数（TP）:  21 
## 真陰性数（TN）:  387 
## 偽陽性数（FP）:  12 
## 偽陰性数（FN）:  24 
## 
## Cut-off: ≥ 5 
##          Actual
## Predicted   0   1
##         0 395  38
##         1   4   7
## 特異度（Sp）:  0.9899749 
## 感度（Se）:  0.1555556 
## 陽性尤度比（LR+）:  15.51667 
## 陰性尤度比（LR-）:  0.8529958 
## 真陽性数（TP）:  7 
## 真陰性数（TN）:  395 
## 偽陽性数（FP）:  4 
## 偽陰性数（FN）:  38 
## 
## Cut-off: ≥ 6 
##          Actual
## Predicted   0   1
##         0 399  42
##         1   0   3
## 特異度（Sp）:  1 
## 感度（Se）:  0.06666667 
## 陽性尤度比（LR+）:  NA 
## 陰性尤度比（LR-）:  0.9333333 
## 真陽性数（TP）:  3 
## 真陰性数（TN）:  399 
## 偽陽性数（FP）:  0 
## 偽陰性数（FN）:  42

スコアモデルでnet benegitを評価する決定曲線を描く　＠開発コホート

dca_optin <-
  decision_curve(hosp_mortality ~ predicted_manual,
                 data = dev_imp,
                 policy = "opt-in",
                 fitted.risk = T,
                 thresholds = seq(0, 0.5 ,by = 0.05))

# データをプロット
plot_decision_curve(dca_optin,
                    curve.names = "Model",
                    legend.position = "topright",
                    confidence.intervals = F,
                    standardize = F)

child-pugh・MELD・年齢でもnetbenefitを比較　@開発コホート

# 初期のモデルのDCAを計算
dca_optin <- decision_curve(hosp_mortality ~ predicted_manual,
                            data = dev_imp,
                            policy = "opt-in",
                            fitted.risk = T,
                            thresholds = seq(0, 0.5 ,by = 0.05))


# 年齢に基づくロジスティック回帰モデルを作成
age_model <- glm(hosp_mortality ~ age, data = dev_imp, family = binomial)

# 予測値（予測確率）を取得
dev_imp$age_predicted <- predict(age_model, newdata = dev_imp, type = "response")

# 年齢に基づくモデルのDCAを計算
dca_age <- decision_curve(hosp_mortality ~ age_predicted,
                          data = dev_imp,
                          policy = "opt-in",
                          fitted.risk = T,
                          thresholds = seq(0, 0.5 ,by = 0.05))


# child_numに基づくロジスティック回帰モデルを作成
child_num_model <- glm(hosp_mortality ~ child_num, data = dev_imp, family = binomial)

# 予測値（予測確率）を取得
dev_imp$child_num_predicted <- predict(child_num_model, newdata = dev_imp, type = "response")

# child_numに基づくモデルのDCAを計算
dca_child_num <- decision_curve(hosp_mortality ~ child_num_predicted,
                                data = dev_imp,
                                policy = "opt-in",
                                fitted.risk = T,
                                thresholds = seq(0, 0.5 ,by = 0.05))


# MELDに基づくロジスティック回帰モデルを作成
meld_model <- glm(hosp_mortality ~ meld, data = dev_imp, family = binomial)

# 予測値（予測確率）を取得
dev_imp$meld_predicted <- predict(meld_model, newdata = dev_imp, type = "response")

# MELDに基づくモデルのDCAを計算
dca_meld <- decision_curve(hosp_mortality ~ meld_predicted,
                           data = dev_imp,
                           policy = "opt-in",
                           fitted.risk = T,
                           thresholds = seq(0, 0.5 ,by = 0.05))


# すべてのモデルをプロット
plot_decision_curve(list(dca_optin, dca_meld, dca_child_num, dca_age),
                    curve.names = c("HOPE-EVL score", "MELD score",  "Child-Pugh score","Age"),
                    legend.position = "topright",
                    confidence.intervals = F,
                    standardize = F)

## Note: When multiple decision curves are plotted, decision curves for 'All' are calculated using the prevalence from the first DecisionCurve object in the list provided.

child-pugh・MELD・年齢でもnetbenefitを比較　@検証コホート

# 初期のモデルのDCAを計算
dca_optin_val <- decision_curve(hosp_mortality ~ predicted_manual,
                            data = val_imp,
                            policy = "opt-in",
                            fitted.risk = T,
                            thresholds = seq(0, 0.5 ,by = 0.05))


# 年齢に基づくロジスティック回帰モデルを作成
age_model_val <- glm(hosp_mortality ~ age, data = val_imp, family = binomial)

# 予測値（予測確率）を取得
val_imp$age_predicted <- predict(age_model_val, newdata = val_imp, type = "response")

# 年齢に基づくモデルのDCAを計算
dca_age_val <- decision_curve(hosp_mortality ~ age_predicted,
                          data = val_imp,
                          policy = "opt-in",
                          fitted.risk = T,
                          thresholds = seq(0, 0.5 ,by = 0.05))


# child_numに基づくロジスティック回帰モデルを作成
child_num_model_val <- glm(hosp_mortality ~ child_num, data = val_imp, family = binomial)

# 予測値（予測確率）を取得
val_imp$child_num_predicted <- predict(child_num_model_val, newdata = val_imp, type = "response")

# child_numに基づくモデルのDCAを計算
dca_child_num_val <- decision_curve(hosp_mortality ~ child_num_predicted,
                                data = val_imp,
                                policy = "opt-in",
                                fitted.risk = T,
                                thresholds = seq(0, 0.5 ,by = 0.05))

# MELDに基づくロジスティック回帰モデルを作成
meld_model_val <- glm(hosp_mortality ~ meld, data = val_imp, family = binomial)

# 予測値（予測確率）を取得
val_imp$meld_predicted <- predict(meld_model_val, newdata = val_imp, type = "response")

# MELDに基づくモデルのDCAを計算
dca_meld_val <- decision_curve(hosp_mortality ~ meld_predicted,
                           data = val_imp,
                           policy = "opt-in",
                           fitted.risk = T,
                           thresholds = seq(0, 0.5 ,by = 0.05))



# すべてのモデルをプロット
plot_decision_curve(list(dca_optin_val, dca_meld_val, dca_child_num_val, dca_age_val),
                    curve.names = c("HOPE-EVL score", "MELD score",  "Child-Pugh score","Age"),
                    legend.position = "topright",
                    confidence.intervals = F,
                    standardize = F)

## Note: When multiple decision curves are plotted, decision curves for 'All' are calculated using the prevalence from the first DecisionCurve object in the list provided.