stepwiseによる変数選択
市田 親正
2023-06-12
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)データの読み込み
data<-read_excel("varix_prediction.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)
)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 × 63] (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 ...
## $ 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")
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)
## 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
## 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 0.0tbl_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 |
| 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 1044 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")
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)
## 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
## 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 |
| 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 717 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)
## 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
## 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 |
| 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 327 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")
# 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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## [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 <]8;;https://github.com/njtierney/naniar/issueshttps://github.com/njtierney/naniar/issues]8;;>.
## 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 <]8;;https://github.com/ropensci/visdat/issueshttps://github.com/ropensci/visdat/issues]8;;>.
## 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")# 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 26 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 ...# 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 58 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 ...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 child_score aptt child_num smoke bmi barthel
## 216 1 1 1 1 1 1 1 1 1 1 1 0
## 72 1 1 1 1 1 1 1 1 1 1 0 1
## 16 1 1 1 1 1 1 1 1 1 0 1 1
## 5 1 1 1 1 1 1 1 1 1 0 0 2
## 25 1 1 1 1 1 1 1 1 0 1 1 1
## 10 1 1 1 1 1 1 1 1 0 1 0 2
## 9 1 1 1 1 1 1 1 1 0 0 1 2
## 1 1 1 1 1 1 1 1 1 0 0 0 3
## 9 1 1 1 1 1 1 1 0 1 1 1 1
## 2 1 1 1 1 1 1 1 0 1 1 0 2
## 1 1 1 1 1 1 1 1 0 1 0 1 2
## 1 1 1 1 1 1 1 1 0 1 0 0 3
## 2 1 1 1 1 1 1 1 0 0 0 1 3
## 1 1 1 1 1 1 1 1 0 0 0 0 4
## 9 1 1 1 1 1 1 0 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 0 1 0 1 1 2
## 1 1 1 1 1 1 1 0 1 0 1 0 3
## 2 1 1 1 1 1 1 0 1 0 0 1 3
## 14 1 1 1 1 1 0 1 0 1 1 1 2
## 2 1 1 1 1 1 0 1 0 1 1 0 3
## 3 1 1 1 1 1 0 1 0 1 0 1 3
## 3 1 1 1 1 1 0 1 0 1 0 0 4
## 1 1 1 1 1 1 0 1 0 0 1 1 3
## 7 1 1 1 1 1 0 1 0 0 0 1 4
## 9 1 1 1 1 1 0 1 0 0 0 0 5
## 1 1 1 1 1 1 0 0 0 1 1 0 4
## 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
## 3 1 1 1 1 0 1 1 1 1 1 0 2
## 2 1 1 1 1 0 1 1 1 1 0 1 2
## 2 1 1 1 1 0 1 0 1 1 1 1 2
## 9 1 1 1 0 1 1 0 1 1 1 1 2
## 3 1 1 1 0 1 1 0 1 1 1 0 3
## 2 1 1 1 0 1 0 0 0 1 1 1 4
## 2 1 1 0 1 1 1 1 1 1 1 1 1
## 2 1 1 0 1 1 1 1 1 1 1 0 2
## 1 1 1 0 1 1 1 1 1 1 0 1 2
## 1 1 1 0 1 1 1 1 1 0 1 1 2
## 1 1 1 0 1 1 1 1 1 0 0 1 3
## 1 1 1 0 1 1 1 1 1 0 0 0 4
## 1 1 1 0 1 1 1 0 1 0 0 1 4
## 1 1 1 0 1 1 0 1 0 1 0 0 5
## 1 1 1 0 1 1 0 0 0 0 0 0 7
## 1 1 1 0 1 0 1 1 1 1 1 1 2
## 2 1 1 0 0 1 1 0 1 1 1 1 3
## 1 1 0 1 1 1 1 1 1 1 1 0 2
## 1 1 0 1 1 1 1 1 1 0 1 0 3
## 3 1 0 1 1 0 1 1 1 1 1 1 2
## 1 1 0 1 0 1 1 0 1 1 1 0 4
## 1 1 0 1 0 1 0 0 0 1 1 1 5
## 1 1 0 1 0 0 1 0 1 1 1 1 4
## 1 1 0 0 0 0 1 0 1 1 1 1 5
## 4 0 1 1 1 1 1 1 1 1 1 0 2
## 1 0 1 1 1 1 1 1 1 0 1 1 2
## 1 0 1 1 1 1 1 1 1 0 1 0 3
## 1 0 1 1 1 1 1 1 0 1 1 1 2
## 1 0 1 1 1 1 1 1 0 0 0 0 5
## 1 0 1 0 1 1 0 1 0 1 1 0 5
## 1 0 0 1 1 1 1 1 1 0 0 1 4
## 1 1 1 1 1 1 1 1 1 1 0 1 2
## 3 1 1 1 1 1 0 1 0 1 1 1 3
## 1 1 0 0 1 0 1 1 1 0 0 1 6
## 1 1 1 1 1 1 1 1 1 1 1 0 3
## 1 1 1 1 1 1 0 1 0 1 1 1 4
## 3 0 1 1 1 1 1 1 1 1 1 0 6
## 1 0 1 1 1 1 0 1 0 0 0 1 9
## 1 0 1 1 1 0 1 1 1 1 0 1 7
## 1 0 1 0 1 1 1 1 1 1 1 1 6
## 1 0 1 0 1 0 1 1 1 1 1 1 7
## 1 0 0 1 1 0 1 1 1 1 1 1 7
## 1 0 0 1 1 0 1 1 1 1 1 0 8
## 2 0 0 1 1 0 1 1 1 1 0 1 8
## 1 0 0 1 1 0 1 1 1 1 0 0 9
## 1 0 0 1 1 0 0 1 0 1 0 1 10
## 1 0 0 1 0 0 1 0 1 1 1 1 9
## 1 0 1 1 1 1 1 1 0 1 1 1 7
## 1 0 1 1 1 1 1 0 1 1 1 1 7
## 1 0 1 1 1 1 0 1 0 1 1 1 8
## 1 0 1 1 0 1 1 0 1 1 0 1 9
## 1 0 1 0 0 1 1 0 1 1 1 1 9
## 1 1 1 1 1 1 1 1 1 1 1 1 1
## 2 1 1 1 1 1 1 1 1 1 0 1 2
## 1 1 1 1 1 1 0 1 0 1 1 1 3
## 1 1 1 1 0 1 1 0 1 1 1 0 6
## 1 1 1 0 0 1 1 0 1 1 0 0 8
## 1 1 0 0 1 0 0 0 0 0 0 0 12
## 1 1 0 0 0 0 1 0 1 1 1 1 8
## 1 1 0 0 1 0 0 0 0 1 1 1 10
## 1 1 1 0 1 1 1 1 1 1 0 1 5
## 1 1 0 1 0 0 1 0 1 1 1 0 12
## 1 1 1 1 1 1 1 1 1 1 1 1 3
## 5 1 0 0 0 0 1 0 1 1 1 1 15
## 4 1 0 0 0 0 0 0 0 1 1 1 17
## 2 0 0 0 0 0 1 0 1 1 1 1 20
## 1 0 0 0 0 0 0 0 0 1 1 1 22
## 32 34 37 39 45 62 63 81 84 85 140 957imp.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): 0.3973894 0.02092398
## difference(s): 0.0005230651 0.00623834
## time: 1.132 seconds
##
## missForest iteration 2 in progress...done!
## estimated error(s): 0.3955067 0.01939846
## difference(s): 0.0001509834 0.001399254
## time: 1.068 seconds
##
## missForest iteration 3 in progress...done!
## estimated error(s): 0.3945409 0.01947556
## difference(s): 6.172943e-05 0.001690765
## time: 1.154 seconds
##
## missForest iteration 4 in progress...done!
## estimated error(s): 0.402882 0.01925298
## difference(s): 7.942041e-05 0.001340951
## time: 1.102 seconds
##
## missForest iteration 5 in progress...done!
## estimated error(s): 0.3981342 0.01953794
## difference(s): 7.829139e-05 0.002273787
## time: 1.071 seconds
##
## missForest iteration 6 in progress...done!
## estimated error(s): 0.4042122 0.0200077
## difference(s): 8.352589e-05 0.00285681
## time: 1.14 secondssummary(imp.mf$ximp)## sex barthel child_score pad stroke dimentia ch_lung rheumati
## M:405 0:188 0: 66 0:535 0:526 0:532 0:525 0:533
## F:131 1:167 1:289 1: 1 1: 10 1: 4 1: 11 1: 3
## 2:181 2:181
##
##
##
## 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.: 7.205
## Median :62.00 Median :22.64 Median : 200.0 Median : 8.265
## Mean :61.23 Mean :22.88 Mean : 299.5 Mean : 8.797
## 3rd Qu.:70.00 3rd Qu.:24.37 3rd Qu.: 410.0 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 :36.65
## 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. : 0.200
## 1st Qu.: 79.75 1st Qu.:44.00 1st Qu.: 71.00 1st Qu.: 0.976
## Median : 90.00 Median :51.00 Median : 82.00 Median : 1.457
## Mean : 87.55 Mean :51.12 Mean : 85.26 Mean : 2.191
## 3rd Qu.: 96.00 3rd Qu.:58.01 3rd Qu.: 97.25 3rd Qu.: 2.678
## Max. :150.00 Max. :93.00 Max. :222.00 Max. :15.800
## 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 : 57.00 Median : 30.00 Median : 7270 Median : 8.400
## Mean : 85.18 Mean : 43.39 Mean : 8314 Mean : 8.588
## 3rd Qu.: 96.00 3rd Qu.: 47.00 3rd Qu.:10300 3rd Qu.:10.100
## Max. :984.00 Max. :562.00 Max. :37700 Max. :16.500
## plt tp alb eGFR
## Min. : 21.0 Min. :2.20 Min. :1.100 Min. : 4.274
## 1st Qu.: 73.0 1st Qu.:5.50 1st Qu.:2.500 1st Qu.: 48.745
## Median : 98.0 Median :6.20 Median :2.881 Median : 67.634
## Mean :110.7 Mean :6.11 Mean :2.831 Mean : 69.269
## 3rd Qu.:134.0 3rd Qu.:6.70 3rd Qu.:3.200 3rd Qu.: 88.284
## Max. :482.0 Max. :9.10 Max. :4.400 Max. :196.029
## bun cre crp pt
## Min. : 2.90 Min. : 0.3300 Min. : 0.0000 Min. : 7.50
## 1st Qu.: 16.10 1st Qu.: 0.6700 1st Qu.: 0.1200 1st Qu.: 40.58
## Median : 23.00 Median : 0.8413 Median : 0.3200 Median : 52.65
## Mean : 28.28 Mean : 1.0575 Mean : 0.7937 Mean : 52.25
## 3rd Qu.: 35.62 3rd Qu.: 1.1400 3rd Qu.: 0.7865 3rd Qu.: 63.52
## Max. :124.80 Max. :10.9500 Max. :18.6370 Max. :107.90
## aptt los
## Min. : 17.60 Min. : 0.00
## 1st Qu.: 26.40 1st Qu.: 6.00
## Median : 28.80 Median : 10.00
## Mean : 32.29 Mean : 13.77
## 3rd Qu.: 33.23 3rd Qu.: 18.00
## Max. :240.00 Max. :159.00md.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
## 536 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#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 62 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 23.1 25.3 23 14.5 22.5 ...
## $ smoke : num 0 0 0 240 0 ...
## $ child_num : num 11 6 8.64 8 7.83 ...
## $ 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 ...
## $ 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
str(data_imp)## 'data.frame': 536 obs. of 62 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 23.1 25.3 23 14.5 22.5 ...
## $ smoke : num 0 0 0 240 0 ...
## $ child_num : num 11 6 8.64 8 7.83 ...
## $ 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 ...
## $ 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 ...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$child_num)),
child_score=factor(child_score, levels = c("0", "1", "2")),
gcs=as.integer(gcs),
cci_num=as.integer(cci_num),
pad=factor(pad),
stroke=factor(stroke),
dimentia=factor(dimentia),
ch_lung=factor(ch_lung),
rheumati=factor(rheumati),
pept_ulcer=factor(pept_ulcer),
dm=factor(dm),
dm_compli=factor(dm_compli),
paralysis=factor(paralysis),
malignancy=factor(malignancy),
meta_tumor=factor(meta_tumor),
aids=factor(aids),
eGFR30=factor(eGFR30),
hd=factor(hd),
hcc=factor(hcc),
alcohol=factor(alcohol),
past_rupture=factor(past_rupture),
antiplate=factor(antiplate),
anticoag=factor(anticoag),
antithro=factor(antithro),
nsaids=factor(nsaids),
steroid=factor(steroid),
beta=factor(beta),
vaso=factor(vaso),
map= as.integer(map),
ffp=factor(ffp),
pc=factor(pc),
albner=factor(albner),
sBP= as.integer(sBP),
dBP= as.integer(dBP),
hr=as.integer(hr),
shock=factor(shock),
los=as.integer(los),
)dev_imp$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 78 129 83 67 48 38 20 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//RtmpLMQ7kI/file12e86492e7d7.htmlstr(dev_imp)## 'data.frame': 536 obs. of 62 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 23.1 25.3 23 14.5 22.5 ...
## $ smoke : int 0 0 0 240 0 0 245 1000 0 0 ...
## $ child_num : int 11 6 9 8 8 9 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 ...
## $ 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)) 22.88 (3.43) 22.97 (3.48) 22.29 (3.03)
## smoke (mean (SD)) 299.43 (527.67) 285.29 (366.79) 389.15 (1093.71)
## child_num (mean (SD)) 8.80 (2.07) 8.46 (1.87) 10.93 (2.02)
## 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 (0.74) 36.68 (0.65) 36.48 (1.16)
## sBP (mean (SD)) 87.53 (15.29) 90.31 (13.39) 69.89 (14.90)
## dBP (mean (SD)) 51.10 (11.74) 52.94 (10.82) 39.40 (10.66)
## hr (mean (SD)) 85.24 (20.74) 82.85 (18.81) 100.38 (25.61)
## bil (mean (SD)) 2.19 (2.04) 1.95 (1.76) 3.71 (2.86)
## ast (mean (SD)) 85.18 (91.14) 79.45 (86.87) 121.52 (108.39)
## alt (mean (SD)) 43.39 (50.13) 41.70 (49.33) 54.11 (54.07)
## wbc (mean (SD)) 8313.80 (4357.53) 8158.33 (4161.96) 9299.86 (5366.73)
## hb (mean (SD)) 8.59 (2.45) 8.66 (2.46) 8.11 (2.38)
## plt (mean (SD)) 110.70 (57.12) 109.14 (51.34) 120.60 (84.92)
## tp (mean (SD)) 6.11 (0.90) 6.16 (0.84) 5.77 (1.16)
## alb (mean (SD)) 2.83 (0.58) 2.91 (0.53) 2.32 (0.62)
## eGFR (mean (SD)) 69.27 (30.50) 72.88 (30.17) 46.36 (21.32)
## bun (mean (SD)) 28.28 (18.46) 27.35 (17.59) 34.14 (22.50)
## cre (mean (SD)) 1.06 (0.91) 0.99 (0.90) 1.46 (0.82)
## crp (mean (SD)) 0.79 (1.61) 0.69 (1.54) 1.44 (1.90)
## pt (mean (SD)) 52.25 (16.69) 53.92 (15.69) 41.61 (18.92)
## aptt (mean (SD)) 32.29 (16.40) 30.68 (11.57) 42.51 (31.88)
## los (mean (SD)) 13.77 (15.28) 14.21 (14.24) 11.01 (20.59)
## sex = F (%) 131 ( 24.4) 111 ( 24.0) 20 ( 27.4)
## barthel (%)
## 0 188 ( 35.1) 181 ( 39.1) 7 ( 9.6)
## 1 167 ( 31.2) 155 ( 33.5) 12 ( 16.4)
## 2 181 ( 33.8) 127 ( 27.4) 54 ( 74.0)
## child_score (%)
## 0 66 ( 12.3) 65 ( 14.0) 1 ( 1.4)
## 1 289 ( 53.9) 270 ( 58.3) 19 ( 26.0)
## 2 181 ( 33.8) 128 ( 27.6) 53 ( 72.6)
## 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)) 0.112 0.210 0.0
## smoke (mean (SD)) 0.118 0.127 0.0
## child_num (mean (SD)) <0.001 1.269 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)) 0.033 0.212 0.0
## sBP (mean (SD)) <0.001 1.442 0.0
## dBP (mean (SD)) <0.001 1.261 0.0
## hr (mean (SD)) <0.001 0.780 0.0
## bil (mean (SD)) <0.001 0.741 0.0
## ast (mean (SD)) <0.001 0.428 0.0
## alt (mean (SD)) 0.049 0.240 0.0
## wbc (mean (SD)) 0.037 0.238 0.0
## hb (mean (SD)) 0.073 0.229 0.0
## plt (mean (SD)) 0.111 0.163 0.0
## tp (mean (SD)) 0.001 0.387 0.0
## alb (mean (SD)) <0.001 1.028 0.0
## eGFR (mean (SD)) <0.001 1.015 0.0
## bun (mean (SD)) 0.003 0.336 0.0
## cre (mean (SD)) <0.001 0.546 0.0
## crp (mean (SD)) <0.001 0.432 0.0
## pt (mean (SD)) <0.001 0.709 0.0
## aptt (mean (SD)) <0.001 0.494 0.0
## los (mean (SD)) 0.097 0.180 0.0
## sex = F (%) 0.627 0.078 0.0
## barthel (%) <0.001 1.079 0.0
## 0
## 1
## 2
## child_score (%) <0.001 1.046 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 | 181 (39%) | 7 (9.6%) |
| 1 | 155 (33%) | 12 (16%) |
| 2 | 127 (27%) | 54 (74%) |
| child_score | ||
| 0 | 65 (14%) | 1 (1.4%) |
| 1 | 270 (58%) | 19 (26%) |
| 2 | 128 (28%) | 53 (73%) |
| 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, 410) | 180 (0.00, 398) |
| 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, 60) | 39 (33.00, 46) |
| hr | 80 (70.00, 93) | 100 (89.00, 112) |
| bil | 1 (0.90, 2) | 3 (1.66, 5) |
| ast | 54 (33.00, 91) | 84 (49.00, 153) |
| alt | 29 (20.00, 45) | 33 (25.00, 64) |
| wbc | 7,170 (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, 134) | 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 (26.00, 32) | 32 (29.10, 44) |
| los | 10 (7.00, 18) | 4 (1.00, 16) |
| 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")
# 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)
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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
## 0 188 ( 35.1) 181 ( 39.1) 7 ( 9.6)
## 1 167 ( 31.2) 155 ( 33.5) 12 ( 16.4)
## 2 181 ( 33.8) 127 ( 27.4) 54 ( 74.0)
## child_score (%) <0.001
## 0 66 ( 12.3) 65 ( 14.0) 1 ( 1.4)
## 1 289 ( 53.9) 270 ( 58.3) 19 ( 26.0)
## 2 181 ( 33.8) 128 ( 27.6) 53 ( 72.6)
## 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 (%) 504 ( 94.0) 452 ( 97.6) 52 ( 71.2) <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 (%) 229 ( 42.7) 167 ( 36.1) 62 ( 84.9) <0.001
## hr_cate = 1 (%) 120 ( 22.4) 81 ( 17.5) 39 ( 53.4) <0.001
## bil_cate = 1 (%) 41 ( 7.6) 23 ( 5.0) 18 ( 24.7) <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 (%) 273 ( 50.9) 235 ( 50.8) 38 ( 52.1) 0.936
## tp_cate = 1 (%) 213 ( 39.7) 176 ( 38.0) 37 ( 50.7) 0.054
## alb_cate = 1 (%) 230 ( 42.9) 173 ( 37.4) 57 ( 78.1) <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 (%) 248 ( 46.3) 201 ( 43.4) 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 (%) 1.079 0.0
## 0
## 1
## 2
## child_score (%) 1.046 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 (%) 1.154 0.0
## hr_cate = 1 (%) 0.810 0.0
## bil_cate = 1 (%) 0.577 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 (%) 0.026 0.0
## tp_cate = 1 (%) 0.257 0.0
## alb_cate = 1 (%) 0.905 0.0
## cre_cate = 1 (%) 0.679 0.0
## crp_cate = 1 (%) 0.486 0.0
## pt_cate = 1 (%) 0.430 0.0
## aptt_cate = 1 (%) 0.552 0.0tbl_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 | 181 (39%) | 7 (9.6%) |
| 1 | 155 (33%) | 12 (16%) |
| 2 | 127 (27%) | 54 (74%) |
| child_score | ||
| 0 | 65 (14%) | 1 (1.4%) |
| 1 | 270 (58%) | 19 (26%) |
| 2 | 128 (28%) | 53 (73%) |
| 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, 410) | 180 (0.00, 398) |
| 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, 60) | 39 (33.00, 46) |
| hr | 80 (70.00, 93) | 100 (89.00, 112) |
| bil | 1 (0.90, 2) | 3 (1.66, 5) |
| ast | 54 (33.00, 91) | 84 (49.00, 153) |
| alt | 29 (20.00, 45) | 33 (25.00, 64) |
| wbc | 7,170 (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, 134) | 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 (26.00, 32) | 32 (29.10, 44) |
| los | 10 (7.00, 18) | 4 (1.00, 16) |
| 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 | 452 (98%) | 52 (71%) |
| cci_cate | 65 (14%) | 13 (18%) |
| bt_cate | 131 (28%) | 16 (22%) |
| sBP_cate | 78 (17%) | 56 (77%) |
| dBP_cate | 167 (36%) | 62 (85%) |
| hr_cate | 81 (17%) | 39 (53%) |
| bil_cate | 23 (5.0%) | 18 (25%) |
| 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 | 235 (51%) | 38 (52%) |
| tp_cate | 176 (38%) | 37 (51%) |
| alb_cate | 173 (37%) | 57 (78%) |
| cre_cate | 37 (8.0%) | 25 (34%) |
| crp_cate | 30 (6.5%) | 17 (23%) |
| pt_cate | 201 (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", "shock",
"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","hosp_mortality")
# 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] "shock: factor"
## [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] "aptt_cate: numeric"
## [1] "hosp_mortality: factor"shockありでの初期変数の決定(臨床的観点から)とstepwiseでの変数選択
set.seed(2023)
initial_vars <- c("sex", "barthel", "pept_ulcer", "dm", "malignancy", "hcc", "alcohol",
"past_rupture", "antithro", "steroid", "beta", "shock",
"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","hosp_mortality")
# fastbwによる変数選択
fit_full <- lrm(hosp_mortality ~ ., data = dev_imp[initial_vars])
fit_reduced <- fastbw(fit_full)
# 選択された変数の表示
print(fit_reduced)##
## Deleted Chi-Sq d.f. P Residual d.f. P AIC
## past_rupture 0.01 1 0.9147 0.01 1 0.9147 -1.99
## antithro 0.05 1 0.8216 0.06 2 0.9693 -3.94
## beta 0.07 1 0.7904 0.13 3 0.9876 -5.87
## hb_cate 0.17 1 0.6837 0.30 4 0.9899 -7.70
## aptt_cate 0.17 1 0.6819 0.47 5 0.9933 -9.53
## sex 0.24 1 0.6224 0.71 6 0.9943 -11.29
## age_cate 0.43 1 0.5112 1.14 7 0.9922 -12.86
## alt_cate 0.56 1 0.4524 1.71 8 0.9887 -14.29
## plt_cate 1.46 1 0.2264 3.17 9 0.9572 -14.83
## alcohol 1.24 1 0.2662 4.41 10 0.9272 -15.59
## bmi_cate 2.02 1 0.1548 6.43 11 0.8432 -15.57
## bil_cate 1.32 1 0.2498 7.75 12 0.8041 -16.25
## hcc 1.49 1 0.2226 9.24 13 0.7545 -16.76
## bt_cate 2.81 1 0.0935 12.05 14 0.6020 -15.95
## malignancy 3.68 1 0.0551 15.73 15 0.4000 -14.27
## pept_ulcer 3.49 1 0.0617 19.22 16 0.2573 -12.78
## alb_cate 3.66 1 0.0559 22.88 17 0.1532 -11.12
## ast_cate 3.75 1 0.0529 26.63 18 0.0863 -9.37
## wbc_cate 2.53 1 0.1114 29.16 19 0.0635 -8.84
## dm 3.42 1 0.0645 32.58 20 0.0375 -7.42
## cre_cate 5.29 1 0.0214 37.87 21 0.0133 -4.13
## steroid 5.30 1 0.0213 43.17 22 0.0045 -0.83
##
## Approximate Estimates after Deleting Factors
##
## Coef S.E. Wald Z P
## Intercept -1.846 0.7229 -2.553 1.068e-02
## barthel=1 0.628 0.5623 1.117 2.641e-01
## barthel=2 1.414 0.4816 2.937 3.312e-03
## shock=1 1.657 0.4181 3.964 7.372e-05
## gcs_cate -1.838 0.5961 -3.084 2.041e-03
## crp_cate 1.135 0.4635 2.449 1.431e-02
##
## Factors in Final Model
##
## [1] barthel shock gcs_cate crp_cateGoodness 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 ~ barthel + shock + gcs_cate + crp_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 -0.000000000000334871
## 2 Brier score 0.080675354129527643
## R2 R2 0.418586466203857921
## 11 AIC 298.733845742880930629ROC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 ~ barthel + shock + gcs_cate + crp_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$fitted, ci=TRUE, direction="auto")## Setting levels: control = 0, case = 1## Setting direction: controls < cases# Plot ROC curve
roc_plot <- ggroc(roc_obj)
# Add the theme you used before. For example, if you used theme_minimal:
roc_plot <- roc_plot + theme_minimal()
# Calculate AUC and its confidence interval
auc_roc <- auc(roc_obj)
ci <- ci.auc(roc_obj)
# Print the ROC plot
print(roc_plot)
# Print the AUC and its confidence interval
cat("AUC for the development logistic model: ", auc_roc, "\n")## AUC for the development logistic model: 0.8780289cat("95% CI for AUC: (", ci[1], ",", ci[2], ")\n")## 95% CI for AUC: ( 0.8400065 , 0.8780289 )# Load necessary libraries
library(pROC)
library(ggplot2)
# Re-run the model using the selected variables from earlier
fit_reduced_model <- lrm(hosp_mortality ~ barthel + shock + gcs_cate + crp_cate, data = dev_imp)
# Calculate fitted probabilities
dev_imp$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: 0.8780289calibration plotの作成
# Create calibration plot
val.prob(dev_imp$fitted, dev_imp$hosp_mortality, g=10, cex=.5)
## Dxy C (ROC) R2
## 0.7560578715346607526 0.8780289357673304318 0.4185864662038579209
## D D:Chi-sq D:p
## 0.2591709784207106848 139.9156444335009155111 NA
## U U:Chi-sq U:p
## -0.0037313432835823016 -0.0000000000001136868 1.0000000000000000000
## Q Brier Intercept
## 0.2629023217042930072 0.0806753541295276433 -0.0000000267358833469
## Slope Emax E90
## 0.9999999596919528777 0.0432344638645756896 0.0308623817664151617
## Eavg S:z S:p
## 0.0103251173109911668 0.0568988067149717586 0.9546258048783312633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 ~ barthel + shock + gcs_cate + crp_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 0.7561 0.7597 0.7461 0.0136 0.7425 1000
## R2 0.4186 0.4306 0.4073 0.0233 0.3953 1000
## Intercept 0.0000 0.0000 -0.0501 0.0501 -0.0501 1000
## Slope 1.0000 1.0000 0.9474 0.0526 0.9474 1000
## Emax 0.0000 0.0000 0.0208 0.0208 0.0208 1000
## D 0.2592 0.2677 0.2512 0.0165 0.2426 1000
## U -0.0037 -0.0037 0.0015 -0.0053 0.0015 1000
## Q 0.2629 0.2714 0.2496 0.0218 0.2411 1000
## B 0.0807 0.0789 0.0824 -0.0035 0.0841 1000
## g 1.8747 1.9700 1.8391 0.1309 1.7438 1000
## gp 0.1748 0.1756 0.1723 0.0033 0.1715 1000print("corrected_AUC")## [1] "corrected_AUC"print(corrected_AUC)## [1] 0.8712354# 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 ~ barthel + shock + gcs_cate + crp_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* -0.000000000000334871 -0.000000009407901 0.0000001446726
## t2* 0.080675354129527643 -0.001629802519338 0.0081454825462
## t3* 0.418586466203857921 0.011453923386884 0.0522775350164
## t4* 298.733845742880930629 -6.332406365650058 26.4061495701321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 26 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 ...# 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 58 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 ...###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
## 270 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
## 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
## 270 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
## 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
## 270 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
## 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
## 270 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
## 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 bmi tp child_score smoke child_num aptt
## 270 1 1 1 1 1 1 1 1 1 1 1 0
## 13 1 1 1 1 1 1 1 1 1 1 0 1
## 9 1 1 1 1 1 1 1 1 1 0 1 1
## 1 1 1 1 1 1 1 1 1 1 0 0 2
## 14 1 1 1 1 1 1 1 1 0 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 0 0 1 2
## 20 1 1 1 1 1 1 1 0 1 0 1 2
## 1 1 1 1 1 1 1 1 0 1 0 0 3
## 7 1 1 1 1 1 1 1 0 0 0 1 3
## 1 1 1 1 1 1 1 1 0 0 0 0 4
## 7 1 1 1 1 1 1 0 1 1 1 1 1
## 8 1 1 1 1 1 1 0 1 1 1 0 2
## 1 1 1 1 1 1 1 0 1 1 0 1 2
## 13 1 1 1 1 1 0 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 0 1 1 0 1 1 2
## 1 1 1 1 1 1 0 0 1 1 1 0 3
## 17 1 1 1 1 0 1 1 1 1 1 1 1
## 2 1 1 1 1 0 1 1 1 1 1 0 2
## 1 1 1 1 1 0 1 1 1 0 1 1 2
## 1 1 1 1 1 0 1 1 1 0 0 1 3
## 1 1 1 1 1 0 1 0 1 1 1 1 2
## 1 1 1 1 1 0 0 1 1 1 1 1 2
## 8 1 1 1 0 1 1 1 1 1 1 0 2
## 2 1 1 1 0 1 1 1 0 1 0 0 4
## 1 1 1 1 0 1 1 1 0 0 0 0 5
## 1 1 1 1 0 1 1 0 1 1 1 0 3
## 1 1 1 1 0 1 1 0 1 0 1 0 4
## 5 1 1 0 1 1 1 1 1 1 1 1 1
## 1 1 0 1 1 1 1 1 0 1 0 1 3
## 2 1 0 1 1 1 1 0 1 1 1 1 2
## 1 1 0 0 1 1 1 0 1 1 1 1 3
## 2 0 1 1 1 1 1 1 1 1 1 1 1
## 1 0 1 0 1 1 1 1 0 1 0 1 4
## 1 1 1 0 1 1 1 1 1 0 1 1 3
## 1 1 0 1 1 1 1 0 1 1 1 1 3
## 6 0 0 0 0 1 1 0 1 1 1 0 15
## 1 0 0 0 0 1 1 0 0 1 0 0 17
## 2 1 1 1 1 1 0 1 1 1 1 1 2
## 1 1 1 1 1 0 1 1 1 1 1 1 3
## 1 1 1 1 1 1 1 1 1 0 1 1 4
## 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 5
## 10 12 15 20 24 30 32 35 42 48 49 397imp.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): 0.4496393 0.01998259
## difference(s): 0.0001284863 0.001548423
## time: 0.853 seconds
##
## missForest iteration 2 in progress...done!
## estimated error(s): 0.4480937 0.01951802
## difference(s): 0.0001457033 0.0004926802
## time: 0.857 seconds
##
## missForest iteration 3 in progress...done!
## estimated error(s): 0.4520882 0.01976926
## difference(s): 0.0000430826 0.0005630631
## time: 0.871 seconds
##
## missForest iteration 4 in progress...done!
## estimated error(s): 0.4460195 0.01977595
## difference(s): 0.00001554856 0.0006334459
## time: 0.858 seconds
##
## missForest iteration 5 in progress...done!
## estimated error(s): 0.443625 0.0182599
## difference(s): 0.00007279451 0.0005630631
## time: 0.856 seconds
##
## missForest iteration 6 in progress...done!
## estimated error(s): 0.4465679 0.01905619
## difference(s): 0.00001796719 0.0005630631
## time: 0.864 seconds
##
## missForest iteration 7 in progress...done!
## estimated error(s): 0.4505507 0.02074618
## difference(s): 0.00003858134 0.0005630631
## time: 0.868 secondssummary(imp.mf_val$ximp)## sex barthel child_score pad stroke dimentia ch_lung rheumati
## M:329 0:178 0: 84 0:443 0:433 0:438 0:440 0:444
## F:115 1:138 1:239 1: 1 1: 11 1: 6 1: 4 1: 0
## 2:128 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 Min. : 5.000
## 1st Qu.:50.00 1st Qu.: 20.76 1st Qu.: 0 1st Qu.: 7.000
## Median :60.00 Median : 23.17 Median : 0 Median : 8.000
## Mean :60.42 Mean : 24.37 Mean : 229 Mean : 8.419
## 3rd Qu.:70.00 3rd Qu.: 26.01 3rd Qu.: 320 3rd Qu.:10.000
## Max. :93.00 Max. :339.76 Max. :2100 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 : 91.00 Median :54.00 Median : 85.00 Median : 1.672
## Mean : 89.95 Mean :54.83 Mean : 88.45 Mean : 2.417
## 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 : 82.01 Mean : 39.96 Mean : 9048 Mean : 8.953
## 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 : 102.50 Median :6.100 Median :2.900 Median : 67.731
## Mean : 117.53 Mean :6.149 Mean :2.912 Mean : 70.803
## 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.: 16.50 1st Qu.:0.650 1st Qu.: 0.1195 1st Qu.: 46.20
## Median : 24.90 Median :0.825 Median : 0.2875 Median : 59.70
## Mean : 28.51 Mean :1.046 Mean : 0.8577 Mean : 59.15
## 3rd Qu.: 36.40 3rd Qu.:1.110 3rd Qu.: 0.7485 3rd Qu.: 72.00
## Max. :107.80 Max. :9.080 Max. :18.9370 Max. :100.20
## aptt los
## Min. : 18.90 Min. : 0.00
## 1st Qu.: 29.88 1st Qu.: 5.00
## Median : 32.40 Median : 7.00
## Mean : 35.85 Mean : 10.67
## 3rd Qu.: 37.20 3rd Qu.: 13.00
## Max. :200.01 Max. :217.00md.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
## 444 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#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 62 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 88.9 95 111 125 ...
## $ dBP : num 67 51.5 49 68 70 ...
## $ hr : num 83 78.4 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 ...
## $ 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 62 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 88.9 95 111 125 ...
## $ dBP : num 67 51.5 49 68 70 ...
## $ hr : num 83 78.4 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 ...
## $ 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 ...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$child_num)),
child_score=factor(child_score, levels = c("0", "1", "2")),
gcs=as.integer(gcs),
cci_num=as.integer(cci_num),
pad=factor(pad),
stroke=factor(stroke),
dimentia=factor(dimentia),
ch_lung=factor(ch_lung),
rheumati=factor(rheumati),
pept_ulcer=factor(pept_ulcer),
dm=factor(dm),
dm_compli=factor(dm_compli),
paralysis=factor(paralysis),
malignancy=factor(malignancy),
meta_tumor=factor(meta_tumor),
aids=factor(aids),
eGFR30=factor(eGFR30),
hd=factor(hd),
hcc=factor(hcc),
alcohol=factor(alcohol),
past_rupture=factor(past_rupture),
antiplate=factor(antiplate),
anticoag=factor(anticoag),
antithro=factor(antithro),
nsaids=factor(nsaids),
steroid=factor(steroid),
beta=factor(beta),
vaso=factor(vaso),
map= as.integer(map),
ffp=factor(ffp),
pc=factor(pc),
albner=factor(albner),
sBP= as.integer(sBP),
dBP= as.integer(dBP),
hr=as.integer(hr),
shock=factor(shock),
los=as.integer(los),
)連続量をカテゴリー化
#新規カテゴリ列を作成
val_imp$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：0str(val_imp)## 'data.frame': 444 obs. of 82 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 0 135 428 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 88 95 111 125 116 102 96 97 71 ...
## $ dBP : int 67 51 49 68 70 84 88 55 54 49 ...
## $ hr : int 83 78 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 ...
## $ 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 1 1 1 1 1 1 1 1 1 0 ...
## $ 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
## 0 178 ( 40.1) 174 ( 43.6) 4 ( 8.9)
## 1 138 ( 31.1) 127 ( 31.8) 11 ( 24.4)
## 2 128 ( 28.8) 98 ( 24.6) 30 ( 66.7)
## child_score (%) <0.001
## 0 84 ( 18.9) 82 ( 20.6) 2 ( 4.4)
## 1 239 ( 53.8) 226 ( 56.6) 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 (%) 139 ( 31.3) 126 ( 31.6) 13 ( 28.9) 0.842
## gcs_cate = 1 (%) 434 ( 97.7) 391 ( 98.0) 43 ( 95.6) 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 (%) 127 ( 28.6) 102 ( 25.6) 25 ( 55.6) <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 (%) 92 ( 20.7) 75 ( 18.8) 17 ( 37.8) 0.005
## hb_cate = 1 (%) 164 ( 36.9) 138 ( 34.6) 26 ( 57.8) 0.004
## plt_cate = 1 (%) 208 ( 46.8) 187 ( 46.9) 21 ( 46.7) 1.000
## tp_cate = 1 (%) 186 ( 41.9) 156 ( 39.1) 30 ( 66.7) 0.001
## alb_cate = 1 (%) 177 ( 39.9) 141 ( 35.3) 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 (%) 1.052 0.0
## 0
## 1
## 2
## child_score (%) 1.014 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 (%) 0.059 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 (%) 0.641 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 (%) 0.431 0.0
## hb_cate = 1 (%) 0.478 0.0
## plt_cate = 1 (%) 0.004 0.0
## tp_cate = 1 (%) 0.575 0.0
## alb_cate = 1 (%) 1.013 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 0.0tbl_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 | 174 (44%) | 4 (8.9%) |
| 1 | 127 (32%) | 11 (24%) |
| 2 | 98 (25%) | 30 (67%) |
| child_score | ||
| 0 | 82 (21%) | 2 (4.4%) |
| 1 | 226 (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 (22.31, 25) |
| smoke | 0 (0.00, 340) | 0 (0.00, 296) |
| 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, 13,833) |
| hb | 9 (7.30, 11) | 8 (6.90, 9) |
| plt | 102 (76.00, 140) | 114 (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 (16.40, 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) | 42 (35.50, 52) |
| los | 7 (5.00, 13) | 6 (2.00, 12) |
| 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 | 126 (32%) | 13 (29%) |
| gcs_cate | 391 (98%) | 43 (96%) |
| 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%) | 25 (56%) |
| 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%) | 17 (38%) |
| hb_cate | 138 (35%) | 26 (58%) |
| plt_cate | 187 (47%) | 21 (47%) |
| tp_cate | 156 (39%) | 30 (67%) |
| alb_cate | 141 (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) | ||
baerthel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 84 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 0 135 428 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 88 95 111 125 116 102 96 97 71 ...
## $ dBP : int 67 51 49 68 70 84 88 55 54 49 ...
## $ hr : int 83 78 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 ...
## $ 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 1 1 1 1 1 1 1 1 1 0 ...
## $ 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 87 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 23.1 25.3 23 14.5 22.5 ...
## $ smoke : int 0 0 0 240 0 0 245 1000 0 0 ...
## $ child_num : int 11 6 9 8 8 9 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 ...
## $ 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 1 1 1 1 1 1 1 1 0 1 ...
## $ 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 0.2985 0.0491 0.0808 0.1244 0.2985 ...
## $ diff2 : num 0.08912 0.00241 0.00653 0.01548 0.08912 ...
## $ (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 84 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 23.1 25.3 23 14.5 22.5 ...
## $ smoke : int 0 0 0 240 0 0 245 1000 0 0 ...
## $ child_num : int 11 6 9 8 8 9 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 ...
## $ 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 1 1 1 1 1 1 1 1 0 1 ...
## $ 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 ~ barthel + shock + gcs_cate + crp_cate, data = dev_imp)
# モデルの係数を表示
coefficients(fit_reduced_model)## Intercept barthel=1 barthel=2 shock=1 gcs_cate crp_cate
## -2.2705171 0.4802429 1.5770981 2.1101229 -2.2710242 1.4483711# 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 ~ barthel + shock + gcs_cate + crp_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
## gcs_cate 0 1 1 -2.27100 0.48224 -3.216200 -1.32580
## Odds Ratio 0 1 1 0.10321 NA 0.040107 0.26558
## crp_cate 0 1 1 1.44840 0.42289 0.619520 2.27720
## Odds Ratio 0 1 1 4.25620 NA 1.858000 9.74960
## barthel - 1:0 1 2 NA 0.48024 0.52127 -0.541430 1.50190
## Odds Ratio 1 2 NA 1.61650 NA 0.581910 4.49030
## barthel - 2:0 1 3 NA 1.57710 0.45345 0.688360 2.46580
## Odds Ratio 1 3 NA 4.84090 NA 1.990400 11.77300
## shock - 1:0 1 2 NA 2.11010 0.37827 1.368700 2.85150
## Odds Ratio 1 2 NA 8.24930 NA 3.930300 17.31400# 新たな予測値を計算
#new_pred <- -2.2705171 +
# 0.5 * dev_imp$barthel1 +
# 1.5 * dev_imp$barthel2 +
# 2 * dev_imp$shock +
# -2.0 * dev_imp$gcs_cate +
# 1.5 * dev_imp$crp_cateスコアを列に加える。
dev_imp$barthel1_score <- ifelse(dev_imp$barthel1 == 1, 1, 0)
dev_imp$barthel2_score <- ifelse(dev_imp$barthel2 == 1, 3, 0)
dev_imp$shock_score <- ifelse(dev_imp$shock == 1, 4, 0)
dev_imp$gcs_score <- ifelse(dev_imp$gcs_cate == 1, -4, 0)
dev_imp$crp_score <- ifelse(dev_imp$crp_cate == 1, 3, 0)
dev_imp$sum_score <- dev_imp$barthel1_score + dev_imp$barthel2_score + dev_imp$shock_score + dev_imp$gcs_score + dev_imp$crp_score
val_imp$barthel1_score <- ifelse(val_imp$barthel1 == 1, 1, 0)
val_imp$barthel2_score <- ifelse(val_imp$barthel2 == 1, 3, 0)
val_imp$shock_score <- ifelse(val_imp$shock == 1, 4, 0)
val_imp$gcs_score <- ifelse(val_imp$gcs_cate == 1, -4, 0)
val_imp$crp_score <- ifelse(val_imp$crp_cate == 1, 3, 0)
val_imp$sum_score <- val_imp$barthel1_score + val_imp$barthel2_score + val_imp$shock_score + val_imp$gcs_score + val_imp$crp_score開発データのROC曲線の描図とAUC
# 開発データセットのROCカーブとAUC
roc_obj_dev <- roc(dev_imp$hosp_mortality, dev_imp$sum_score)## Setting levels: control = 0, case = 1## Setting direction: controls < casesplot(roc_obj_dev, main="ROC curve for the development data")
# Calculate AUC and its confidence interval for development data
auc_roc_dev <- auc(roc_obj_dev)
ci_dev <- ci.auc(roc_obj_dev)
# Print the AUC and its confidence interval for development data
cat("AUC for the development data: ", auc_roc_dev, "\n")## AUC for the development data: 0.8775851cat("95% CI for AUC (development): (", ci_dev[1], ",", ci_dev[2], ")\n")## 95% CI for AUC (development): ( 0.8394229 , 0.8775851 )検証データのROC曲線の描図とAUC
# 検証データセットのROCカーブとAUC
roc_obj_val <- roc(val_imp$hosp_mortality, val_imp$sum_score)## Setting levels: control = 0, case = 1## Setting direction: controls < casesplot(roc_obj_val, main="ROC curve for the validation data")
# Calculate AUC and its confidence interval for validation data
auc_roc_val <- auc(roc_obj_val)
ci_val <- ci.auc(roc_obj_val)
# Print the AUC and its confidence interval for validation data
cat("AUC for the validation data: ", auc_roc_val, "\n")## AUC for the validation data: 0.8677249cat("95% CI for AUC (validation): (", ci_val[1], ",", ci_val[2], ")\n")## 95% CI for AUC (validation): ( 0.8189511 , 0.8677249 )開発データ・検証データを共に描図する
# 開発データと検証データのROCオブジェクトの計算
roc_obj_dev <- roc(dev_imp$hosp_mortality, dev_imp$sum_score)## Setting levels: control = 0, case = 1## Setting direction: controls < casesroc_obj_val <- roc(val_imp$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$sensitivities)))
)
# Plot ROC curve
library(ggplot2)
ggplot(data = roc_data, aes(x = Specificity, y = Sensitivity, color = ROC)) +
geom_line() +
scale_x_reverse(limits = c(1, 0)) +
labs(x = "Specificity", y = "Sensitivity", title = "ROC curves") +
theme_minimal() +
scale_color_manual(values = c("Development" = "blue", "Validation" = "red")) +
annotate("text", x = 0.75, y = 0.25, label = paste("AUC for Development = ", round(auc_dev, 2)), color="blue") +
annotate("text", x = 0.75, y = 0.15, label = paste("AUC for Validation = ", round(auc_val, 2)), color="red")
# dev_impデータセットのsum_scoreのヒストグラム
hist(dev_imp$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$sum_score, main = "Histogram of total score for validation data", xlab = "sum_score")
## calibration
スコアリング前のモデルでのcalibration評価
まずはモデル式を定義
#モデルの構築
dev_imp <-
dev_imp|>
mutate(hosp_mortality=as.numeric(hosp_mortality)) #numericにしないと回帰が回らない
val_imp <- val_imp |>
mutate(hosp_mortality = as.numeric(hosp_mortality) - 1) #valは1,2になってしまっているため対処
model <- glm(hosp_mortality ~ barthel + shock + gcs_cate +crp_cate, family = binomial, data = dev_imp)
# サマリー統計量の取得
summary_stats <- summary(model)
# 係数(coefficient)、標準誤差(SE)、およびp値の取得
coefficients <- summary_stats$coefficients
# 各項目の結果を表示
print(coefficients)## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.2705171 0.6399523 -3.5479473 0.0003882458953
## barthel1 0.4802429 0.5212677 0.9212981 0.3568948301549
## barthel2 1.5770981 0.4534406 3.4780696 0.0005050387733
## shock1 2.1101229 0.3782694 5.5783599 0.0000000242797
## gcs_cate -2.2710242 0.4822417 -4.7093073 0.0000024856009
## crp_cate 1.4483711 0.4228898 3.4249373 0.0006149413370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$predicted <- pred_dev
# スコアごとに要約
dev_by <- dplyr::group_by(dev_imp, sum_score)
table_dev <- dplyr::summarize(dev_by,
hosp_mortality = sum(hosp_mortality), # outcomeは１/0
n = n(),
mortality_rate = round(hosp_mortality / n, 3),
prediction = round(mean(predicted), 3)
)
table_dev## # A tibble: 13 × 5
## sum_score hosp_mortality n mortality_rate prediction
## <dbl> <dbl> <int> <dbl> <dbl>
## 1 -4 0 131 0 0.011
## 2 -3 3 102 0.029 0.017
## 3 -1 3 63 0.048 0.048
## 4 0 5 47 0.106 0.08
## 5 1 4 53 0.075 0.125
## 6 2 1 7 0.143 0.18
## 7 3 30 95 0.316 0.3
## 8 4 3 6 0.5 0.391
## 9 5 1 1 1 0.579
## 10 6 7 11 0.636 0.648
## 11 7 12 15 0.8 0.805
## 12 8 1 1 1 0.854
## 13 10 3 4 0.75 0.946検証データ,モデル式でのcalibrationのための割合比較
# 予測確率の計算
pred_val <- predict(model, newdata = val_imp, type = "response")
val_imp$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: 11 × 5
## sum_score hosp_mortality n mortality_rate prediction
## <dbl> <dbl> <int> <dbl> <dbl>
## 1 -4 1 109 0.009 0.011
## 2 -3 0 81 0 0.017
## 3 -1 1 43 0.023 0.049
## 4 0 3 67 0.045 0.08
## 5 1 5 39 0.128 0.124
## 6 2 0 5 0 0.18
## 7 3 20 66 0.303 0.298
## 8 4 6 11 0.545 0.377
## 9 5 0 1 0 0.579
## 10 6 7 17 0.412 0.649
## 11 7 2 5 0.4 0.805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 >= -4 & sum_score <= -1 ~ "-4 to -1",
sum_score >= 0 & sum_score <= 2 ~ "0 to 2",
sum_score >= 3 & sum_score <= 4 ~ "3 to 4",
sum_score >= 5 & sum_score <= 7 ~ "5 to 7",
sum_score >= 8 & sum_score <= 10 ~ "8 to 10"
))
# '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 >= -4 & sum_score <= -1 ~ "-4 to -1",
sum_score >= 0 & sum_score <= 2 ~ "0 to 2",
sum_score >= 3 & sum_score <= 4 ~ "3 to 4",
sum_score >= 5 & sum_score <= 7 ~ "5 to 7",
sum_score >= 8 & sum_score <= 10 ~ "8 to 10"
))
# 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
# 'shock' 列を数値に変換します
dev_imp$shock <- as.numeric(as.character(dev_imp$shock))
# 手動で予測値を計算します
liner_predict_manual <- -2.2705171 + 0.5 * dev_imp$barthel1 +1.5 * dev_imp$barthel2 +2 * dev_imp$shock -2.0 * dev_imp$gcs_cate +1.5 * dev_imp$crp_cate
pred_manual <- exp(liner_predict_manual) / (exp(liner_predict_manual) + 1)
# 新しい予測値でテーブルを作成します
dev_imp$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: 13 × 5
## sum_score hosp_mortality n mortality_rate prediction_manual
## <dbl> <dbl> <int> <dbl> <dbl>
## 1 -4 0 131 0 0.014
## 2 -3 3 102 0.029 0.023
## 3 -1 3 63 0.048 0.059
## 4 0 5 47 0.106 0.094
## 5 1 4 53 0.075 0.145
## 6 2 1 7 0.143 0.219
## 7 3 30 95 0.316 0.316
## 8 4 3 6 0.5 0.433
## 9 5 1 1 1 0.557
## 10 6 7 11 0.636 0.675
## 11 7 12 15 0.8 0.774
## 12 8 1 1 1 0.849
## 13 10 3 4 0.75 0.939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 >= -4 & sum_score <= -1 ~ "-4 to -1",
sum_score >= 0 & sum_score <= 2 ~ "0 to 2",
sum_score >= 3 & sum_score <= 4 ~ "3 to 4",
sum_score >= 5 & sum_score <= 7 ~ "5 to 7",
sum_score >= 8 & sum_score <= 10 ~ "8 to 10"
))
# 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(mortality_rate = 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(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_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("mortality_rate" = "blue", "prediction" = "red")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
スコアモデル 検証データでのcaliration評価
# 'shock' 列を数値に変換します
val_imp$shock <- as.numeric(as.character(val_imp$shock))
# 手動で予測値を計算します
liner_predict_manual_val <- -2.2705171 + 0.5 * val_imp$barthel1 +1.5 * val_imp$barthel2 +2 * val_imp$shock -2.0 * val_imp$gcs_cate +1.5 * val_imp$crp_cate
pred_manual_val <- exp(liner_predict_manual_val) / (exp(liner_predict_manual_val) + 1)
# 新しい予測値でテーブルを作成します
val_imp$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: 11 × 5
## sum_score hosp_mortality n mortality_rate prediction_manual
## <dbl> <dbl> <int> <dbl> <dbl>
## 1 -4 1 109 0.009 0.014
## 2 -3 0 81 0 0.023
## 3 -1 1 43 0.023 0.059
## 4 0 3 67 0.045 0.094
## 5 1 5 39 0.128 0.145
## 6 2 0 5 0 0.219
## 7 3 20 66 0.303 0.316
## 8 4 6 11 0.545 0.433
## 9 5 0 1 0 0.557
## 10 6 7 17 0.412 0.675
## 11 7 2 5 0.4 0.774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 >= -4 & sum_score <= -1 ~ "-4 to -1",
sum_score >= 0 & sum_score <= 2 ~ "0 to 2",
sum_score >= 3 & sum_score <= 4 ~ "3 to 4",
sum_score >= 5 & sum_score <= 7 ~ "5 to 7",
sum_score >= 8 & sum_score <= 10 ~ "8 to 10"
))
# 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(mortality_rate = 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(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_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("mortality_rate" = "blue", "prediction" = "red")) +
theme_minimal() +
theme(plot.title = element_text(hjust = 0.5))
## netbenefitの計算
liner_predict_manual <- -2.2705171 + 0.5 * dev_imp$barthel1 +1.5 * dev_imp$barthel2 +2 * dev_imp$shock -2.0 * dev_imp$gcs_cate +1.5 * dev_imp$crp_cate
pred_manual <- exp(liner_predict_manual) / (exp(liner_predict_manual) + 1)
roc_obj <- roc(dev_imp$hosp_mortality, pred_manual)## Setting levels: control = 0, case = 1## Setting direction: controls < casesthreshold <- 0.7 # この値は臨床的な意義に基づいて設定する必要があります
net_benefit <- sum(roc_obj$sensitivities * (1 - threshold)) - sum(roc_obj$specificities * threshold)
print(net_benefit)## [1] -5.208367曲線を描く
# Calculate the coordinates of the ROC curve
roc_coords <- pROC::coords(roc_obj, seq(0, 1, by = 0.01), transpose = FALSE)
# Calculate the net benefit for the model
net_benefit_model <- roc_coords$sensitivity - roc_coords$specificity * (1 - seq(0, 1, by = 0.01)) / seq(0, 1, by = 0.01)
# Calculate the net benefit for the "All" strategy
net_benefit_all <- roc_coords$sensitivity
# Calculate the net benefit for the "None" strategy
net_benefit_none <- rep(0, length(seq(0, 1, by = 0.01)))
# Combine the net benefits into one data frame
net_benefit_data <- data.frame(
Threshold = seq(0, 1, by = 0.01),
Model = net_benefit_model,
All = net_benefit_all,
None = net_benefit_none
)
# Plot the decision curve
ggplot(net_benefit_data, aes(x = Threshold)) +
geom_line(aes(y = Model, color = "Model")) +
geom_line(aes(y = All, color = "All")) +
geom_line(aes(y = None, color = "None")) +
labs(x = "Threshold Probability", y = "Net Benefit", color = "Strategy") +
theme_minimal()## Warning: Removed 1 row containing missing values (`geom_line()`).