I am using a cox proportional hazards model to run a survival analysis in R. The analysis involves analyzing which biochemical markers predict mortality in buffalo. I am using id as a mixed effect to account for individual heterogeneity.
My full model is:
mod.1 <- coxme(Surv(start, stop, death) ~ ALB+ALP+AST+CA+GGT+TP+GLOB+BUN+CK+PHOS+MG+ age.independent+I(age.independent^2)+sex+bcs+ (1|id), data=data)
I was wondering if anyone has any thoughts on what the best way to go about conducting model selection on fixed effects in R when you have a large number of covariates?