varix sensitivity analysis

必要なlibraryの読み込み

library(tidyverse)

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## ✔ lubridate 1.9.2     ✔ tidyr     1.3.0
## ✔ purrr     1.0.1     
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library(readxl)
library(tableone)
library(lubridate)
library(skimr)
library(svglite)
library(haven)
library(summarytools)

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library(naniar)

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library(devtools)

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library(reader)

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library(stringr)
library(missForest)
library(mice)

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library(geepack)
library(ggplot2)
library(cobalt)

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library(tableone)
library(sandwich)
library(survey)

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library(WeightIt)
library(twang)

## To reproduce results from prior versions of the twang package, please see the version="legacy" option described in the documentation.

library(naniar)
pacman::p_load(norm2, miceadds, lmtest, car, ROCR, pROC)

データ読み込み

 data <- read_excel("varix_icu_ok.xlsx")

データの整理

df <-
  data|> 　#miceパッケージが順序ありの因子型でないと読めないためこれを指定。
  mutate(
        hosp_id=as.integer(hosp_id),
        year=as.integer(year),
        pt_id=as.integer(pt_id),
        ad_num=as.integer(ad_num),
        antibio_exposure=as.integer(antibio_exposure),　#geeglmを回すにはfactorでは回らない
        antibio_type_a= factor(antibio_type_a, levels = c("a", "b","c","d","e","f","g","h","i","99")),
        antibio_type_b= factor(antibio_type_b, levels = c("c","d","f","h","i","99")),
        antibio_term= as.integer(antibio_term ),
        age_num=as.integer(age_num),
        age_cate=factor(age_cate,levels = c("0", "1", "2", "3")),
        sex= factor(sex, levels = c("M", "F")),
        smoking= as.integer(smoking),
        brthel_cate= factor(brthel_cate, levels = c("0", "1", "2")),
        child_numl= as.integer(child_numl),
        child_score=factor(child_score, levels = c("a", "b", "c")),
        jcs_all=factor(jcs_all, levels = c("0", "1", "2","3","10","20","30","100","200","300")),
        cci_num=as.integer(cci_num),
        malignancy=factor(malignancy),
        hd=factor(hd),
        hcc=factor(hcc),
        alocohol=factor(alocohol),
        past_varix_rup=factor(past_varix_rup),
        antiplate_user_01=factor(antiplate_user_01),
        anticoag_user01=factor(anticoag_user01),
        nsaids_user01=factor(nsaids_user01),
        steroid_user01=factor(steroid_user01),
        ppi_user01=factor(ppi_user01),
        vaso=factor(vaso),
        map_day0= as.integer(map_day0),
        shock=factor(shock),
        eGFR30=factor(eGFR30),
        outcome=factor(outcome),
        death_day2=factor(death_day2),
        rebleed_end_hemo=factor(rebleed_end_hemo),
        sbp=factor(sbp),
        infection=factor(infection),
        infection_all=factor(infection_all),
        los=as.integer(los)
         )

多重代入を行う

# Perform multiple imputation
imp <- mice(df, m = 100, maxit = 200, method = "pmm",  printFlag = FALSE, seed = 10)

## Warning: Number of logged events: 360000

抗菌薬投与期間による　感度分析　IPTW ATTによる　

# Define the conditions for the datasets
condition_names <- c("Unrestricted", "2 days or more", "3 days or more", "4 days or more")

results <- list()

for (j in 1:length(condition_names)) {
  condition_name <- condition_names[j]
  
  condition_results <- list()
  
  for (i in 1:100) {
    imputed_data <- complete(imp, i)
    
    # Filter data based on condition
    if (condition_name != "Unrestricted") {
      if (j == 2) {
        imputed_data <- imputed_data %>% filter(!(antibio_term == 1))
      } else if (j == 3) {
        imputed_data <- imputed_data %>% filter(!(antibio_term == 1 | antibio_term == 2))
      } else if (j == 4) {
        imputed_data <- imputed_data %>% filter(!(antibio_term == 1 | antibio_term == 2 | antibio_term == 3))
      }
    }
    
    # Fit GEE propensity score model
    propensity_model <- geeglm(antibio_exposure ~ age_cate + sex + brthel_cate + child_numl + cci_num + malignancy + hd + hcc + alocohol + past_varix_rup + antiplate_user_01 + anticoag_user01 + nsaids_user01 + steroid_user01 + ppi_user01 + vaso + map_day0 + shock + t_bil + ast + alt + wbc + hb + plt + alb + eGFR30 + crp + pt + aptt, 
                               data = imputed_data, 
                               id = hosp_id, 
                               family = binomial("logit"), 
                               corstr = "exchangeable")
    
    # Calculate propensity scores
    ps <- predict(propensity_model, type = "response", newdata = imputed_data)
    imputed_data$ps <- ps
    
    # Calculate IPTW and fit the logistic regression model
    stabilized_weights <- with(imputed_data, antibio_exposure + (1 - antibio_exposure) * ps / (1 - ps))
    iptw_model <- glm(outcome ~ antibio_exposure, data = imputed_data, weights = stabilized_weights, family = binomial("logit"))
    
        # Store the results
    condition_results[[i]] <- list(
      Estimate = coef(iptw_model)[2],
      Std.Error = coef(summary(iptw_model))[2, "Std. Error"],
      Conf.Int = confint(iptw_model, level = 0.95, method = "Wald")
    )
  }
  
  # Calculate the pooled estimates
  Qbar <- mean(sapply(condition_results, function(x) x$Estimate))
  se_Qbar <- sqrt(sum(sapply(condition_results, function(x) x$Std.Error^2))/length(condition_results))

  # Calculate the pooled odds ratio and confidence intervals
  pooled_or <- exp(Qbar)
  pooled_lower <- exp(Qbar - 1.96 * se_Qbar)
  pooled_upper <- exp(Qbar + 1.96 * se_Qbar)

  # Calculate the pooled p-value (two-tailed)
  pooled_p <- 2 * (1 - pnorm(abs(Qbar / se_Qbar)))

  # Store the results in a data frame
  results[[condition_name]] <- data.frame(
    Condition = condition_name,
    OR = pooled_or,
    Lower_CI = pooled_lower,
    Upper_CI = pooled_upper,
    P_value = pooled_p
  )
}

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# Combine the results into a single data frame
results_table <- do.call(rbind, results)

# Display results as a table
print(results_table)

##                     Condition        OR  Lower_CI Upper_CI   P_value
## Unrestricted     Unrestricted 1.0678232 0.5415712 2.105441 0.8497386
## 2 days or more 2 days or more 1.0512703 0.4967254 2.224910 0.8960015
## 3 days or more 3 days or more 0.9773650 0.4284329 2.229620 0.9566075
## 4 days or more 4 days or more 0.8408114 0.3412937 2.071424 0.7062330

# Create forest plot data
forest_plot_data <- data.frame(
  Outcome = results_table$Condition,
  OR = results_table$OR,
  Lower_CI = results_table$Lower_CI,
  Upper_CI = results_table$Upper_CI,
  P_value = results_table$P_value
)

# Create forest plot
forest_plot_ordered <- ggplot(forest_plot_data, aes(x = Outcome, y = OR, ymin = Lower_CI, ymax = Upper_CI)) +
  geom_pointrange(size = 0.5, fatten = 0.5) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  scale_y_log10(limits = c(0.3, 5)) + 
  coord_flip() +
  theme_bw() +
  labs(
    x = "Condition",
    y = "Odds Ratio (95% Confidence Interval)"
  ) +
  geom_text(aes(label = sprintf("p = %.3f", P_value)), vjust = -1.0, size = 3) +
  scale_x_discrete(limits = c("4 days or more", "3 days or more", "2 days or more", "Unrestricted"))

# Display the forest plot
print(forest_plot_ordered)

感度分析第3セフェムのみ

data3 <- read_excel("varix_icu_ok_third_cefem.xlsx")
df3 <-
  data3|> 　#miceパッケージが順序ありの因子型でないと読めないためこれを指定。
  mutate(
        hosp_id=as.integer(hosp_id),
        year=as.integer(year),
        pt_id=as.integer(pt_id),
        ad_num=as.integer(ad_num),
        antibio_exposure=as.integer(antibio_exposure),　#geeglmを回すにはfactorでは回らない
        antibio_type_a= factor(antibio_type_a, levels = c("d","e","99")),
        antibio_type_b= factor(antibio_type_b, levels = c("f","h","99")),
        antibio_term= as.integer(antibio_term ),
        age_num=as.integer(age_num),
        age_cate=factor(age_cate,levels = c("0", "1", "2", "3")),
        sex= factor(sex, levels = c("M", "F")),
        smoking= as.integer(smoking),
        brthel_cate= factor(brthel_cate, levels = c("0", "1", "2")),
        child_numl= as.integer(child_numl),
        child_score=factor(child_score, levels = c("a", "b", "c")),
        jcs_all=factor(jcs_all, levels = c("0", "1", "2","3","10","20","30","100","200","300")),
        cci_num=as.integer(cci_num),
        malignancy=factor(malignancy),
        hd=factor(hd),
        hcc=factor(hcc),
        alocohol=factor(alocohol),
        past_varix_rup=factor(past_varix_rup),
        antiplate_user_01=factor(antiplate_user_01),
        anticoag_user01=factor(anticoag_user01),
        nsaids_user01=factor(nsaids_user01),
        steroid_user01=factor(steroid_user01),
        ppi_user01=factor(ppi_user01),
        vaso=factor(vaso),
        map_day0= as.integer(map_day0),
        shock=factor(shock),
        eGFR30=factor(eGFR30),
        outcome=factor(outcome),
        death_day2=factor(death_day2),
        rebleed_end_hemo=factor(rebleed_end_hemo),
        sbp=factor(sbp),
        infection=factor(infection),
        infection_all=factor(infection_all),
        los=as.integer(los)
         )

セフェム3多重代入

imp3 <- mice(df3, 
             m=100,　#mは代入の結果として作成するデータセットの数
             maxit =200,　#maxitは反復回数
             method="pmm",
             printFlag = FALSE,
             seed = 10
             )

## Warning: Number of logged events: 360000

セフェム3 メインATT解析フォレストプロットは作成するが使うのはcomposite だけでよいか。

# Function to perform IPTW analysis on different outcomes
perform_iptw_analysis <- function(outcome_name) {
  gee_results <- list()
  summary_tables <- list()
  conf_ints <- list()
  weighted_data <- list()
  ps_data <- list()

  for (i in 1:100) {
    imputed_data <- complete(imp3, i)
    
    # Fit GEE propensity score model
    propensity_model <- geeglm(antibio_exposure ~ age_cate + sex + brthel_cate + child_numl + cci_num + malignancy + hd + hcc + alocohol + past_varix_rup + antiplate_user_01 + anticoag_user01 + nsaids_user01 + steroid_user01 + ppi_user01 + vaso + map_day0 + shock + t_bil + ast + alt + wbc + hb + plt + alb + eGFR30 + crp + pt + aptt, 
                               data = imputed_data, 
                               id = hosp_id, 
                               family = binomial("logit"), 
                               corstr = "exchangeable")
    
    # Calculate propensity scores
    ps <- predict(propensity_model, type = "response", newdata = imputed_data)
    imputed_data$ps <- ps
    
    # Calculate IPTW and fit the logistic regression model by ATT
   stabilized_weights <- with(imputed_data, antibio_exposure + (1 - antibio_exposure) * ps / (1 - ps))
    iptw_model <- glm(as.formula(paste0(outcome_name, " ~ antibio_exposure")), data = imputed_data, weights = stabilized_weights, family = binomial("logit"))
    
    # Store the results
    gee_results[[i]] <- iptw_model
    summary_tables[[i]] <- summary(iptw_model)
    conf_ints[[i]] <- confint(iptw_model, level = 0.95, method = "Wald")
    
    # Store weighted data
    imputed_data$weight <- stabilized_weights
    weighted_data[[i]] <- imputed_data
    ps_data[[i]] <- imputed_data[, c("ps", "antibio_exposure")]
  }
  
  # Save the results from each imputed dataset
  params <- list()
  for (i in 1:100) {
params[[i]] <- data.frame(Estimate = coef(gee_results[[i]])[2],
Std.Error = coef(summary_tables[[i]])[2, "Std. Error"],
df = Inf)
}
#Calculate the pooled estimates
Qbar <- mean(sapply(params, function(x) x$Estimate))
se_Qbar <- sqrt(sum(sapply(params, function(x) x$Std.Error^2))/length(params))

#Calculate the pooled odds ratio and confidence intervals
pooled_or <- exp(Qbar)
pooled_lower <- exp(Qbar - 1.96 * se_Qbar)
pooled_upper <- exp(Qbar + 1.96 * se_Qbar)

#Calculate the pooled p-value (two-tailed)
pooled_p <- 2 * (1 - pnorm(abs(Qbar / se_Qbar)))

return(list(OR = pooled_or, Lower_CI = pooled_lower, Upper_CI = pooled_upper, P_value = pooled_p, PS_data = ps_data))
}

#Perform IPTW analysis for each outcome
outcomes <- c("outcome", "death_day2", "rebleed_end_hemo", "sbp")
results <- lapply(outcomes, perform_iptw_analysis)

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#Combine results into a data frame for plotting
forest_plot_data <- data.frame(
Outcome = outcomes,
OR = sapply(results, function(x) x$OR),
Lower_CI = sapply(results, function(x) x$Lower_CI),
Upper_CI = sapply(results, function(x) x$Upper_CI),
P_value = sapply(results, function(x) x$P_value)
)

#Display results as a table
print(forest_plot_data)

##            Outcome        OR  Lower_CI  Upper_CI   P_value
## 1          outcome 1.1624426 0.4195120  3.221059 0.7722202
## 2       death_day2 0.7541932 0.2121503  2.681153 0.6628785
## 3 rebleed_end_hemo 1.7112876 0.2926484 10.006907 0.5510055
## 4              sbp 1.4734510 0.2120218 10.239787 0.6951534

#Create forest plot with the specified order and adjusted p-value position
forest_plot_ordered <- ggplot(forest_plot_data, aes(x = Outcome, y = OR, ymin = Lower_CI, ymax = Upper_CI)) +
geom_pointrange(size = 0.5) +
geom_hline(yintercept = 1, linetype = "dashed") +
scale_y_log10() +
coord_flip() +
theme_bw() +
labs(
x = "Outcome",
y = "Odds Ratio (95% Confidence Interval)"
) +
geom_text(aes(label = sprintf("p = %.3f", P_value)), vjust = -1.0, size = 3) +
scale_x_discrete(limits = c("sbp", "rebleed_end_hemo", "death_day2","outcome"), labels = c("SBP", "Rebleeding", "In hospital mortality", "Composite outcome"))

#Display forest plot
forest_plot_ordered

セフェム3 ATTで負の二項分布で解析してみる

# Install and load the MASS package
if (!requireNamespace("MASS", quietly = TRUE)) {
  install.packages("MASS")
}
library(MASS)

## 
##  次のパッケージを付け加えます: 'MASS'

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

# LOS
# 本解析: IPTW models on imputed data with Negative Binomial distribution
gee_results <- list()
summary_tables <- list()
conf_ints <- list()
weighted_data <- list()

for (i in 1:100) {
  imputed_data <- complete(imp3, i)
  
  # Fit GEE propensity score model
  propensity_model <- geeglm(antibio_exposure ~ age_cate + sex + brthel_cate + child_numl + cci_num + malignancy + hd + hcc + alocohol + past_varix_rup + antiplate_user_01 + anticoag_user01 + nsaids_user01 + steroid_user01 + ppi_user01 + vaso + map_day0 + shock + t_bil + ast + alt + wbc + hb + plt + alb + eGFR30 + crp + pt + aptt, 
                             data = imputed_data, 
                             id = hosp_id, 
                             family = binomial("logit"), 
                             corstr = "exchangeable")
  
  # Calculate propensity scores
  ps <- predict(propensity_model, type = "response", newdata = imputed_data)
  imputed_data$ps <- ps
  
  # Calculate IPTW and fit the negative binomial regression model by ATT
  stabilized_weights <- with(imputed_data, antibio_exposure + (1 - antibio_exposure) * ps / (1 - ps))
  iptw_model <- glm.nb(los ~ antibio_exposure, data = imputed_data, weights = stabilized_weights)
  
  # Store the results
  gee_results[[i]] <- iptw_model
  summary_tables[[i]] <- summary(iptw_model)
  conf_ints[[i]] <- confint(iptw_model, level = 0.95, method = "Wald")
  
  # Store weighted data
  imputed_data$weight <- stabilized_weights
  weighted_data[[i]] <- imputed_data
}

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## Waiting for profiling to be done...
## Waiting for profiling to be done...
## Waiting for profiling to be done...

# Save the results from each imputed dataset
params <- list()
for (i in 1:100) {
  params[[i]] <- data.frame(estimate = coef(gee_results[[i]])[2], 
                            std.error = coef(summary_tables[[i]])[2, "Std. Error"],
                            df = Inf) # Add infinite degrees of freedom
}

# Manually pool the results
Qbar <- mean(sapply(params, function(x) x$estimate))
se_Qbar <- sqrt(sum(sapply(params, function(x) x$std.error^2))/length(params))

# Calculate the pooled rate ratio and confidence intervals
pooled_rr <- exp(Qbar)
pooled_lower <- exp(Qbar - 1.96 * se_Qbar)
pooled_upper <- exp(Qbar + 1.96 * se_Qbar)

# Calculate the pooled z-value
pooled_z <- Qbar / se_Qbar

# Calculate the pooled p-value (two-tailed)
pooled_p <- 2 * (1 - pnorm(abs(pooled_z)))

cat("\nPooled results (IPTW with Negative Binomial distribution)\n")

## 
## Pooled results (IPTW with Negative Binomial distribution)

cat("\nRate Ratio:", pooled_rr, "\n")

## 
## Rate Ratio: 0.9380726

cat("\n95% Confidence Interval: (", pooled_lower, ", ", pooled_upper, ")\n")

## 
## 95% Confidence Interval: ( 0.7456054 ,  1.180222 )

cat("\nP-value:", pooled_p, "\n")

## 
## P-value: 0.5853045

varix sensitivity analysis

市田　親正

2023/4/20

感度分析第3セフェムのみ

varix sensitivity analysis

市田 親正

2023/4/20

感度分析 第3セフェムのみ

市田　親正

感度分析第3セフェムのみ