Generalized linear mixed model procedures, such as GENLINMIXED, are designed to model data which come from a family of exponential distributions, conditional upon normally distributed random effects. Your data appear to fall under that umbrella. Admittedly, some of your terminology (e.g., where you use the terms bivariate and multivariate) is a bit confusing to me, but I will simply chalk it up to semantics.



I see merit in treating both clinic and patient zip code as random effects. Whether it is possible to do so using the GENLINMXED procedure is something you will have to test. (I would use the GLIMMIX procedure in SAS for this type of analysis due to its ability to employ a superior estimation method, and if too computationally intensive, I would likely use the MCMC procedure in SAS.) If permitted in GENLINMIXED, you should add RANDOM statement where the subject is "zipcode."



Side note: Since I can't see your dataset, let me just say that you want your subject identification variable specification to result in each subject having a unique identifier. For example, you don't want GENLINMIXED to treat the first person in the first zipcode to be treated as the same person who happens to be the first person in second zipcode. I don't think that will happen based on your subject*clinic subject identification specification, but just make sure that is true. If I were you, I would just create a unique identifier for each subject that starts at 1 and ends at N number of subjects.



With the inclusion of the second RANDOM statement, it would be acceptable to add fixed effects predictors at the patient-level, clinic-level, and now patient zipcode-level.



Good luck.



Ryan