Displaying 1 result from an estimated 1 matches for "all_covari".
2010 Nov 02
2
multi-level cox ph with time-dependent covariates
...other words: names(data) = patient_id, start_time, stop_time,
tissue_id, pathogen_type, marker1, ..., marker100, ..., outcome
If I had multiple observations per patient at different time
intervals, I would model it like this (hope it is correct)
model<-coxph(Surv(start_time,stop_time,outcome)~all_covariates+cluster(patient_id))
But now I have both the patient and the tissue, and hundreds of
different variables. I thought I could use the coxme library, since it
has also a ridge regression feature. Shall I then model nested random
effects by considering both the patient_id and the tissue_id?
Like...