I have a research question where I wanted to investigate factors predicting a change in a severity score of a lung health indicator (let's call it Y). I have two measurements which are unequally spaced, with subjects having an average follow-up 7.7 years; but some subjects have a follow-up period of 5 years and others close 9 years. I have covariates that are 'fixed' (e.g. sex, race) and others that are collected at each of the two time points (e.g. bmi). My question is: how can I best model the outcome of interest Y? If I am correct, I don't think I should worry about the co-variance structure in the model or 'random' time effects since I only have two time points. However, I think it is important to take into account that not all subjects have the same follow-up time (and perhaps treat time as continuous?). I have tried the following with SAS code, but here I treated time as a categorical variable (0/1) -- which I believe is not correct since not everyone has the same follow-up time. proc mixed data=data_long;
class id sex time;
model Y = time sex bmi time*bmi;
repeated time / type = un sub=id;
*lsmeans time*bmi;
run;
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