如何编写一个函数或循环来遍历这个使用mstate（）包的过程，它使用了多个嵌套框架（cohort 1-cohort 25）？
样本数据：

impute1 <- data.frame(unique_ID = c(1,2,3,4), 
                  DIED_INDICATOR = c(0,1,1,1), 
                  CVD_ANY = c(0,1,1,0), 
                  YEARS_CVD_DEATH = c(15.9, 23.6, 22.7, 3.4), 
                  YEARS_CVD_HOSP = c(15.9, 11.4, 22.7, 3.4), 
                  TOBACCO = c(0, 0, 0, 1), 
                  MARRIED = c(1,0,1,0), 
                  PARITY = c(2,1,1,2)) 

impute2 <- data.frame(unique_ID = c(1,2,3,4), 
                  DIED_INDICATOR = c(0,1,1,1), 
                  CVD_ANY = c(0,1,1,0), 
                  YEARS_CVD_DEATH = c(15.9, 23.6, 22.7, 3.4), 
                  YEARS_CVD_HOSP = c(15.9, 11.4, 22.7, 3.4), 
                  TOBACCO = c(0, 1, 0, 1), 
                  MARRIED = c(1,0,1,1), 
                  PARITY = c(1,1,1,2)) 

covs<-c("TOBACCO", "MARRIED", "PARITY")

字符串
在1个框架上运行模型的代码：

cohort1 <- msprep(data=impute1,trans=tmat, 
             time=c(NA,"YEARS_CVD_HOSP","YEARS_CVD_DEATH"),
             status=c(NA,"CVD_ANY","DIED_INDICATOR"), 
             keep=covs,
             id = as.vector(impute1$unique_ID))

cohort_expand<-expand.covs(cohort1, covs, append = TRUE, longnames = FALSE)

c1<-coxph(Surv(Tstart, Tstop, status)~TOBACCO.1 + TOBACCO.2 + 
TOBACCO.3 + strata(trans),     data=cohort1, method = "breslow")
summary(c1)

newdata<-data.frame(trans=1:3, TOBACCO.1 = c(0,0,0), TOBACCO.2 = 
c(0,0,0), TOBACCO.3 = c(0,0,0), strata = 1:3)

msf1<-msfit(c1, newdata, trans=tmat)

plot(msf1, las=1, lty=rep(1:2,c(8,4)), xlab="Years")

型
我把25个字符串放入一个列表中：

path <- ''
print(path)
files <- list.files(path = path, pattern="*.sas7bdat", full.names=FALSE)
print(files)

impute <- list()
for (i in 1:length(files)){
  filename <- paste0(path, files[i])
  print(filename)
  impute[[i]] <- haven::read_sas(data_file=filename)
  print(names(impute[[i]]))
  eval(parse(text = paste0("impute", i, " <- 
haven::read_sas(data_file=filename)")))
}

型
我正在尝试使用前两个估算数据集为msprep步骤编写一个函数。

test_list <- list(impute1, impute2)

my_func <- function(x) {
    cohort<-mstate::msprep(data=test_list,trans=tmat, 
               time=c(NA,"YEARS_CVD_HOSP","YEARS_CVD_DEATH"),
               status=c(NA,"CVD_ANY","DIED_INDICATOR"), 
               keep=covs,
               id = as.vector(test_list$unique_ID))
}

test<-lapply(test_list, my_func)

型
我得到一个错误：
mstate中出错：：msprep（data = test_list，transs = tmat，time = c（NA，“YEARS_CVD_HOSP”，：参数“id”不是向量
如何将unique_ID指定为向量，即使它是一个列表？
下一个功能：

test<-lapply(test_list, my_func2)

my_func2 <- function(x) {
             cohort<-mstate::cohort<-expand.covs(cohort, covs, append = 
             TRUE, longnames = FALSE)
}

型
我也尝试过使用lapply函数来迭代coxph模型，但是我如何编写整个过程的代码呢？

c1<- lapply(mydf , function(i) {
  
 iformula <- as.formula(sprintf("Surv(Tstart, Tstop, status) 
~TOBACCO.1 + TOBACCO.2 + TOBACCO.3 +   strata(trans)", i))  

})