I am creating a GEE in PROC GLIMMIX (I asked about this model yesterday), and I am having trouble understanding the difference in output produced by the Solutions for Fixed Effects and Type III Tests of Fixed Effects.   My model has 4 fixed effects and 1 interaction. All fixed effects are binary including the outcome. I am most interested in location, sex, and their interaction - var3 and var4 are just variables I need to control for. I would like the odds ratios with confidence intervals for all fixed effects including location and sex. I ran the following estimate statements to do so. The estimates I ran are not equal to those from the Solutions table, and the p-values match that of the Type III Tests rather than the Solutions table.   Here are my questions: 1) Are the odds ratios for each fixed effect the exponentiated estimates in the Solutions table or the exponentiated estimates I ran? 2) What is the difference between the p-values in the Solutions table compared to the Type III Tests?   Appreciate your help. proc GLIMMIX data = DATA ORDER=INTERNAL EMPIRICAL=root; class ID Cluster Location(REF='1') Sex(REF='1') Var3(ref='1') Var4(ref='1'); model OUTCOME(event='1')= Location|Sex Var3 Var4      / dist=bin link=logit covb solution; ESTIMATE 'Location' Location1 -1 / exp cl; ESTIMATE 'Sex' Sex 1 -1 / exp cl; RANDOM _residual_ / SUBJECT=Cluster TYPE=cs vcorr solution; run; ... View more

I am creating a GEE, but my question is really about logistic modeling. Do all covariates need to be included when calculating probabilities?   I am using SAS PROC GLIMMIX. My model has 4 fixed effects and 1 interaction. I am most interested location, sex, and their interaction - var3 and var4 are just variables I need to control for. I outputted the following table using an LSMEANS statement. SAS's default is to output the estimates, but I would like to make them probabilities. Can I simply run them through the inverse logit or is that an issue since it is not taking into account var3 and var4? Appreciate the help. proc GLIMMIX data = DATA ORDER=INTERNAL EMPIRICAL=root; class ID Cluster Location(REF='1') Sex(REF='1') Var3(ref='1') Var4(ref='1'); model OUTCOME(event='1')= Location|Sex Var3 Var4 / dist=bin link=logit covb solution; RANDOM _residual_ / SUBJECT=Cluster TYPE=cs vcorr solution; LSMEANS Location*Sex/SLICE=Location E cl diff odds oddsratio ilink lines; run;     ... View more