Hi everyone,
I’m using a discrete time model (Gompertz model/logistic regression using cloglog-Link function which equals the Cox interval-censored model) to estimate the effect of a treatment on the uptake of aftercare. I have an RCT with 5 measurement points (baseline, 3 weeks, 6 weeks, 3 months, 6 months after randomization). My intention is to test the hypothesis, that the type of treatment has an effect on the uptake of aftercare (intervention vs control/TAU).
In Cox PH models, I know that adjusting for as many covariates as possible (without overfitting) is the best possible way to test a hypothesis and to get the most accurate values for hypothesis testing. In how far is this the case in the discrete time model using Gompertz regression? Should I also adjust for as many covariates as possible (theoretical background assured)?