Pooling Cox PH results after multiple imputation with the MICE package

Question

I have a dataset with survival data and a few missing covariates. I've successfully applied the mice-package to imputate m-numbers of datasets using the mice() function, created an imputationList object and applied a Cox PH model on each m-dataset. Subsequently I'ved pooled the results using the MIcombine() function. This leads to my question:

How can I get a p-value for the pooled estimates for each covariate? Are they hidden somewhere within the MIcombine object?

我知道 p 值并不是一切，但报告估计值和置信区间而没有相应的 p 值对我来说似乎很奇怪。我能够计算一个近似值。使用例如Altman 提供的公式来自置信区间的 p 值，但这似乎过于复杂。我四处寻找答案，但我什至找不到任何人提到这个问题。我是否忽略了一些明显的东西？

例如：

library(survival)
library(mice)
library(mitools)
test1 <- as.data.frame(list(time=c(4,3,1,1,2,2,3,5,2,4,5,1), 
          status=c(1,1,1,0,1,1,0,0,1,1,0,0), 
          x=c(0,2,1,1,NA,NA,0,1,1,2,0,1), 
          sex=c(0,0,0,0,1,1,1,1,NA,1,0,0)))

dat <- mice(test1,m=10)

mit <- imputationList(lapply(1:10,complete,x=dat))

models <- with(mit,coxph(Surv(time, status) ~ x + strata(sex)))

summary(MIcombine(models))

我试图对 MIcombine 对象的结构进行排序，但到目前为止还没有找到 p 值的运气。

Answer 1

models <- with(dat,coxph(Surv(time, status) ~ x + strata(sex)))
summary(pool(models))