I have this project that in the original method, the person suggested to use a generalized linear mixed model with a logit link, and I need to follow this approach. The response variable is a binary variable for Remission Response (y/n); All subjects get the same treatment at Week 0. And the project wants to look at the effectiveness of the treatment in achieving the Remission response in a real world evidence setting in three different stratum (bionaive, 1 prior biologic, 2 or more prior biologic) The Remission Response measured repeatedly at week Weeks 6, 16, 26, 36, and 52 for each subject Another variable Stratum, which is a categorical variable with three levels (biologic naive, 1 prior biologic, 2 or more prior biologic) Timepoint(Weeks) and Stratum will be included as fixed effect. Least square means odds ratio and 95% CI need to be calculated for each stratum. My model would be:   PROC GENMOD DATA=Test; CLASS Remission Weeks Stratum USUBJID; MODEL Remission=Weeks Stratum Weeks*Stratum /DIST=BIN LINK=LOGIT; LSMEANS Stratum*Weeks; Repeated USUBJID; or    PROC GLIMMIX DATA=Test; CLASS Remission Weeks Stratum USUBJID; MODEL Remission=Weeks Stratum Weeks*Stratum /DIST=BIN LINK=LOGIT; LSMEANS Stratum*Weeks; Random USUBJID; RUN;   My questions are: 1. Can you use PROC GENMOD for a generalized linear mixed model? Would both model work? (I don't have the data yet) 2. Can someone explain if we need a mixed model for such repeated measures? I don't understand why the person originally suggested use a generalized linear mixed model where I did not see any random effect to be included.    ... View more