Dear R-list, I am wondering how to perform a bootstrap in R for the weighted time dependent Cox model? (Andersen?Gill format, with multiple observations from each patients) to obtain the bootstrap standard error of the treatment effect. Below is an example dataset. Would 'censboot' be appropriate to use in this context? Any suggestions/references/direction to R-package will be highly appreciated. Thanks Ehsan ###########################
dataset = read.csv("http://stat.ubc.ca/~e.karim/dataset2.csv")
head(dataset) # (tx = treatment, weight = IPTW)
id tx enter exit event weight 1 1 0 0 1 0 1.037136 2 1 0 1 2 0 1.299079 3 1 0 2 3 0 1.352642 4 1 1 3 4 0 1.245575 5 1 0 4 5 0 1.360458 6 1 0 5 6 0 1.236780
time.dep.weighted.cox = coxph(Surv(enter, exit, event) ~ tx + cluster(id), robust = TRUE, data = dataset, weights = weight) time.dep.weighted.cox
coef exp(coef) se(coef) robust se z p tx -0.2 0.819 0.22 0.25 -0.798 0.42 Likelihood ratio test=0.83 on 1 df, p=0.361 n= 9626, number of events= 81