Hi Everyone, I could use some help getting PROC GLIMMIX (or another SAS procedure, if more appropriate) to model some correlated binary data. This is patient data, where the outcome is "yes" or "no" (did the patient have the event in question).  Other variables of interest are baseline measures (height, weight, etc.), physician conducting procedure, and hospital where physician conducted procedure.  Some patients have more than one observation, while others have only one.  So, I want to account for correlation within the same patient, within the same physician, and within the same hospital. Here is the code I am using: proc glimmix data=events;      class patient passes physician priors site related;      model related = priors passes postwt bmi/dist=binary link=logit ddfm=bw solution;      random _residual_/subject=patient solution; /*patient is repeated measure*/      random physician/ solution; /*physician and site are random effects*/      random site/solution; run; When I use this code, depending on which covariates I use, I sometimes get no estimates (solutions for random effects) for site and/or for physician.  This makes me wonder how valid the model is.  If I just use physician or site as a fixed effect, the model does not converge.  Also, this doesn't seem quite like the correct way to model the data. I would like to know whether certain physicians are associated with events of interest and also whether certain hospitals are associated with events of interest.  This is in addition to the relationship between covariates like height, weight, and age and events of interest. Is there a more appropriate way for me to model this data?  I am not sure that the syntax I am using is actually achieving the desired model. Many thanks in advance! ... View more