I'm currently struggling with the implementation of a generalized linear mixed effects model in SAS using PROC GLIMMIX, and would like to ask for some support: My endpoint variable is a response repeatedly measured (once per week) for each subject of different treatment groups. I would like to fit a GLMM with binomial distribution, logit link function, random intercept and fixed terms of treatment group, week and treatment by week interaction interaction as explanatory variables, using the subjects’ by-week responder status as the response variable. The  main issue is to fit the model so that a subject-specific random intercept is assumed and the repeated structure of the data is considered. After extended exploration of GLIMMIX, I ended up with the following solution, but I'm not sure if that really reflects the analysis appropriately: PROC GLIMMIX DATA = resp; CLASS subject_id treatment week; MODEL response = treatment week treatment*week /DIST=binomial LINK=logit OR SOLUTION; RANDOM week /SUBJECT=subject_id TYPE=un RESIDUAL; RUN; According to PROC GLIMMIX documentation, this should reflect the repeated structure of the data appropriately, but we are not convinced, if this also correctly reflects the random intercept. For PROC MIXED I know that it’s sufficient in such a situation to have the REPEATED statement specified, and that no additional RANDOM statement is needed. Does that also hold here for PROC GLIMMIX? Does the proposed code what I want to do? I also tried the following, to have the random intercept and repeated included: … RANDOM intercept/ SUBJECT=subject_id; RANDOM week / SUBJECT=subject_id TYPE=un RESIDUAL; …. but here problems with convergency of the model (also independent of the TYPE used) occurred, and I do not know if this is a real “no convergence” due to observed data constellation, or if the problem of non-convergence comes from an incorrect model specifications. Thanks a lot for any support ... View more