I am using GLIMMIX to estimate a mixed model (with random effect for subject) for a binary outcome. My question concerns interpretation of results from a test conducted using the CONTRAST statement (using multiple variables in the contrast statement). I am trying to explain, in simple terms, how this test assesses changes in the predictive ability of the model. I know that in regular logistic models, it essentially assesses the change in the log likelihood estimate of the overall model with vs. without the parameters; however, in GLIMMIX, from what I understand, the log likelihood comparison is not valid (given that they are really only pseudo log likelihoods). A SAS resource (see here) cautions against comparing log likelihood estimates. The GLIMMIX output for the contrast statement provides a F statistic and p-value. What exactly is being compared across the nested models? In my output, the F-statistic equals the difference in the pseudo log likelihoods, but I thought this was an invalid comparison? Also, how do I determine the overall statistical significance of the GLIMMIX model? My output gives me the residual log likelihood and the generalized chi-square, but no p-value. Are no significance values given in GLIMMIX models? Below is an example of the code I am running: Proc glimmix data=[dataset] method=rspl noclprint oddsratio; class subject; model vaccination(event='1') = gender age race income attitude1 attitude2 /dist=bin link=logit solution cl ddfm=none; contrast 'F test of attitudes' attitude1 1 attitude2 1; Random int/subject=ego; ESTIMATE 'male vs female' gender 1 / EXP adjust=sidak; run;
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