Thank you once again for the quick reply. I am interested in both differences between group A and B at time 1 and 2, and the change from time 1 to 2 in each group. I also want to evaluate the change from time 0 to 1 in group A. I have log transformed my biomarker data, is this sufficient? Or would you still recommend using PROC GLIMMIX ? I have added some LSMESTIMATE statements which give me the estimates I am interested in (this gives the same estimates as using the LSMEANS time*group/diff statement). PROC MIXED DATA=long; CLASS id group time; MODEL biomarker=time*group/ CL DDFM=KR2 VCIRY OUTPM=fitmain RESIDUAL; REPEATED time / SUBJECT=id(group) TYPE=UN R RCORR; LSMESTIMATE time*group "B vs A at time 1" 0 1 0 -1 0 /cl; LSMESTIMATE time*group "B vs A at time 2" 0 0 1 0 -1 /cl; LSMESTIMATE time*group "A at time 1 vs time 0" 1 -1 0 0 0 /cl; LSMESTIMATE mate time*group "A at time 2 vs time 1" 0 -1 1 0 0/cl; LSMESTIMATE time*group "B at time 2 vs time 1" 0 0 0 -1 1 / cl; RUN; However, I am still puzzled. I see that for group A, the mean biomarker value decreases over time. However, in the PROC MIXED model, I get an increase in the biomarker estimates between time 1 and 2 in group A. Any idea what could cause this? I have tried to fit different covariance structures, and the AICC was smallest with the unstructured covariance. Best, -Jules
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