Hi, I am using Proc genmod to analyse outcome variable 'has insurance' and contrasting those with pre-existing conditions to those without before and after a policy was implemented using below code: ods graphics on; proc genmod data=thesis.mydata; class preex_status year_class / ref=first; weight perweight1; model coverage_status(event= "1")= sex_dummy age_dummy race_dummy employment_dummy marital_status poverty_status year_class preex_status year_class*preex_status / dist=binomial link=identity; estimate "Diff in Diff" preex_status*year_class 1 -1 -1 1; lsmeans preex_status*year_class; lsmestimate preex_status*year_class "Diff in Diff" 1 -1 -1 1; run; ods graphics off; In the model statement I am controlling for confounders with dummy variables. I get the below output. My question is who does this 0.9% represent? I know it is those without pre-existing conditions but other than that how do I know other demographic characteristic (race, age, sex, income etc?) I know I am controlling for White, female, employed, married etc. So, the the LS Means output and the contrast estimates is for whom (What does SAS take as default here)? This is the first time I am doing DiD and Genmod any input is highly appreciated. Output of above code is attached. Thanks a lot
... View more
