survival probabilities with cph (counting process)
I have fitted a cox model with time-varying covariates (counting process style)
using the cph function of the Design package. Now I want to know the survival probabilities at each time point given the history of a single individual.
I know the survest function, but I am not sure how to interpretet its output when using time-varying covariates. Does it just give the probabilities as if it are independent individuals or can/does it take in consideration that it is the history of a single individual? Is this even possible?
An example: Individual x has a history of 3 months and the cox model is fitted with two time-varying covariates: a & b
testcase <- data.frame(a =[4 5 2], b = [1 0 1])
survest(coxmodel, testcase, time = c(1,2,3))
Is this the right way to compute the probabilities?