Dear all, I would like to know if it is possible to fit in R a Cox ph model with time-dependent covariates and to account for hierarchical effects at the same time. Additionally, I'd like also to know if it would be possible to perform any feature selection on this model fit.
I have a data set that is composed by multiple marker measurements (and hundreds of covariates) at different time points from different tissue samples of different patients. Suppose that the data were coming from animal model with very few subjects (n=6) that were followed up given a pathogen exposure, measured several times, sampling different tissues in the same days, until a certain outcome was reached (or outcome censored). Suppose that the pathogen can vary over time (might be a bacteria that selects for drug-resistance) and that also it can vary across different tissue reservoirs within the same patient. In 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 model<-coxme(Surv(start_time,stop_time,outcome) ~ covariates + (1 | patient_id/tissue_id)) Then, how could I shrink the coefficients in order to select a subset of them with non-neglegible effects? May I also consider the possibility to run an AIC-based forward-backward selection? thanks and apologies if I am completely out of the trails, M.P. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.