I've seen others searching for similar issues, but have not yet come across a example that explains how to actually do this:
I have a dataset with both time varying and non-time varying variables that I want to examine using an extended Cox model. Some of the observations have missing non-varying variables. All time-dependent variables are complete.
My dataset have a form like this:
t1 - t2 - surv - id - varying - nonvarying 1 - 2 - 1 - 1 - 2 - 1 2 - 3 - 1 - 1 - 1 - 1 3 - 4 - 0 - 1 - 2 - 1 1 - 2 - 1 - 2 - 5 - NA 2 - 3 - 1 - 2 - 2 - NA 3 - 4 - 0 - 2 - 1 - NA
As you can see - if the nonvarying variable is missing, it's missing across all observations with the same ID. Although I don't know what the value of the missing variable is (because it's missing...), I know that it doesn't vary for all the observations with the same ID.
So far I've managed to make imputations of non-varying dataset using the MICE package in R. But I have not yet found a way of doing this with a dataset with time-dependent variables so that it takes into account that the non-varying variable that are imputed are in fact not varying across observations with the same ID.
In my searches I've come across this presentation that might address the problem: https://people.maths.bris.ac.uk/~mb13434/prst_talks/R_Keogh_160205_PrSt_Bristol.pdf
But I've still not figured how to solve this problem. And I'm not familiar with the suggested package in the presentation (smcfcs) - that means, I don't understand how to use that package to calculate MI that takes time-dependent and non-time dependent variables into account keeping the non-time dependent variable constant across observations of the same subject.
Anyone else got a clue about how to achieve this?