I am new to both GEE and mixed modeling, so please bare with me:
Briefly: my exposure is television viewing in childhood (tv) and I am trying to assess change in body mass index (bmisds) over 3 time (time) points - ages 4, 12 and 13 with adjustment for covariates. I first ran the analysis as a generalized estimating equation using proc genmod. When I run this analysis as a mixed effects model (with a random intercept and time treated as a random effect), I am essentially getting extremely similar/same parameter estimates to the GEE. Am I doing something wrong? This is the code I used:
proc mixed data=test; class tv(ref="2.00") M_ID mom_gc(ref="1.00") brfed(ref="2.00") preec(ref="0.00") gender_merged firstborn sectioyes mat_ed activityhs4; model bmisds=time tv time*tv mom_gc brfed preec gender_merged firstborn sectioyes mat_ed activityhs4 GA_weeks MBMIsvkon1 maternal_age_birth weightsds_1/s corrb; random int time/subject=M_ID; run;
If it is right, why are the estimates so similar and which one do you recommend using? As I understand it, the GEE gives you population effects but the mixed effects model gives you both population averages and subject-specific effects, so how would I interpret an interaction for tv (2hours)*time with an estimate of 0.20 for example in the mixed effects model above? Instead of saying "children who watch TV for >2 hours had, on average, a 0.20-unit higher body mass index over time" as I might for the GEE, would I say "a child who watches >2 hours of TV has a 0.20-unit higher body mass index over time"? Where do the random effects come in? I am not seeing anything "extra" in the output of the mixed effects model over the GEE..