Going back to your original question which I didn't see at the time, you asked about fitting a fixed effects model. And in fact, either a fixed effects model (also called a conditional logistic model) or a Generalized Estimating Equations (GEE) model are reasonable approaches for dealing with your situation of having data from each of many facilities if you assume that the observations within a facility are correlated while observations across facilities are not. As mentioned in this note on the types of logistic models available, the fixed effects model can be fit by specifying your facilities variable in the STRATA statement. The GEE model can be fit in PROC GENMOD by specifying your facilities variable in the REPEATED statement. Both approaches have the benefit of accounting for the correlation within facilities but avoid adding a set of parameters to the model for each of the facilities. If instead of one of these methods you include your facilities variable in the CLASS and MODEL statements, the resulting unconditional model must estimate the entire set of facility parameters and this can cause estimation problems such as separation. You can see examples of both models in the LOGISTIC or GENMOD documentation. Also see the book by Allison on the fixed effects model referenced in the note I mention above. It has a chapter on this model in the binary response setting.
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