Good evening everyone,   I am new to hierarchical logistic regression and would like to have your help on a practical problem. I need to analyse potential predictors of early recanalisation (dependent variable, binary) in an observational study of 352 patients with acute stroke. Patients had brain imaging and thrombolysis therapy at the nearest stroke center (nearest_center, n=15) and all patients were subsequently transferred to a comprehensive stroke center (comprehensive_center, n=4) to receive endovascular therapy. One important point: sometimes the nearest center was a comprehensive stroke center and secondary transfer was obviously not needed.   I consider using a mixed model to take into account potential heterogeneity across centers.  When I run a three-level hierarchical model with proc glimmix (level-1 = patient; level-2= nearest center; level-3 = comprehensive center), I have no convergence problem. Here is the code that I used (random intercept model, fixed slopes). Thrombus_length is a continuous variable and fvh is a binary variable:     proc glimmix method=laplace ; class comprehensive_center nearest_center fvh (ref='0') ; model recanalisation_complete (event='1') = thrombus_length fvh / dist=binary link=logit solution oddsratio ; random intercept / subject=comprehensive_center ; random intercept / subject=nearest_center(comprehensive_center) ; run;    My first question is: is it correct to use such a model since for some patients the comprehensive center and nearest center are the same ?   I also considered using a simpler model, with two hierarchical levels (level-1=patient; level-2= nearest center):   proc glimmix method=laplace ; class nearest_center fvh (ref='0') ; model recanalisation_complete (event='1') = thrombus_length fvh / dist=binary link=logit solution oddsratio ; random intercept / subject=nearest_center ; run; However, in this situation I get the following message: "Warning: obtaining minimum var. quadratic unbiased estimates as starting values for the covariance parameters failed." I tried many options proc glimmix offers for controlling the optimization process (http://support.sas.com/resources/papers/proceedings12/332-2012.pdf), without success. The culprit seems to be the thrombus_length variable, because I get the above error message only when this variable is in the model (even if it is the only independant variable entered).    My second question is: could this error message reflect that the number of patients (or the number of patients who recanalize) in each nearest_center is too small to reasonably allow the use of a hierarchical logistic model ? Is there a rule of thumb regarding this point ? The number of patients in each nearest_center ranges from 6 to 44, and the number of patients who recanalize ranges from 0 to 11 (total number of patients who recanalize: 61).   Many thanks for your help. Guillaume ... View more