I´m working on a time-to-event modell in R using the coxph function in the survival package. I´m analysing animal movements until they pass the study area which consists of 2 zones. Each individual can have multiple events before the final passing event as they move between zones, each zone is analysed seperatley. I was thinking that stratifying the model on number of zone exposures or adding frailty(individual id) as a random effect would controll for this and give somewhat similar results.
However the results are widely different when looking at model fit (AIC). Covariates with a large effect in the stratified model barley have very little impact in the frailty model. The coeficients and the significance levels are more similar though.
It feels like there is something wrong or should this large difference be expected from the two different approaches?
Is any of the approaches prefered when controling for multipel events? Or is there a better way than these two approaches?
(range number of events events 1-11, mean number of events 2.24)
Example code:
frailty_model <- coxph(formula=Surv(T1,T2,event==2) ~ day_night + discharge + frailty(id), data = cox_passage, na.action = na.fail)
stratified_model <- coxph(formula=Surv(T1,T2,event==2) ~ day_night + discharge + strata(n exposures witin zone), data = cox_passage, na.action = na.fail)