I am working on longitudinal data and currently trying to test the potential preventive effect of a class of antihypertensive drugs on the evolution of cognitive performances over time.
Patients were followed 7 times (each 6 months) and data regarding drug consumption, many covariates as well as cognition were collected at each time (visit).
I initially thought to do a linear mixed model with the antihypertensive class at baseline and the cognition at each time as the outcome and adjust on different confounders.
However, many patients changed of antihypertensive drugs over time. Thus, only considering the class at baseline is not a very good method.
- Is considering the treatment as a time varying covariate a good idea?
- Would you recommend other kind of models ? I heard about functional data analysis but I do not know at all how to implement it?
- I would like to use propensity score matching but the fact that I am working on panel data strongly complicates the situation and I do not know how to use propensity scores in panel data?
I am using STATA 15.
Someome to help me a little bit to start with this problem?
Thank you ever so much for your time and consideration,
Dr Pierre M.