I'm looking at the longitudinal outcome for two treatment groups using both population averaged model and mixed effect (random intercept and slope) model. I've got opposite results on the fixed effect of treatment. I thought the covariance structure could bias the fixed effect estimates as indicated in Gruka et al. (2011). But I've tried unstructured and many other structures, still the results are opposite. Do any of you know the reason?
Below are brief SAS code and results:
* population averaged model: proc mixed data=test; class id interval; model y=treatment|time/solution; repeated interval/subject=id type=ar(1); run; Effect Estimate Error Pr > |t| Intercept 0.53 0.02 <.0001 trt -0.10 0.03 0.0004 time -0.005 0.001 0.0017 trt*time -0.003 0.002 0.185 * mixed model; proc mixed data=test; class id; model y=treatment|time/solution; random intercept time/subject=id type=un; run; Effect Estimate Error Pr > |t| Intercept 0.1602 3.91 <.0001 trt 0.4869 8.58 <.0001 time 0.003110 0.95 0.3443 trt*time -0.01894 -4.17 <.0001