Since you want the clinic odds ratio for each sex and for each race, I assume that you want to allow for the clinic odds ratio to change in each sex and each race.  If so, then you model needs to accommodate that. You could then just use the ODDSRATIO statement to get the clinic odds ratios for each combination of sex and race. But you seem to want them for sex alone and for race alone. To do that you have to be comfortable averaging over the other variable (that is, over race for the odds ratios for sex). You can do that using the SLICE statement. For example, using C, S, R for Clinic, Sex, and Race: proc logistic plots=none; class c s r / param=glm; model disease(event='Yes')=c c*s c*r; oddsratio c; slice c*s / sliceby=s diff oddsratio cl; slice c*r / sliceby=r diff oddsratio cl; run; ... View more

Just add the clinic*race interaction in the MODEL and LSMEANS statements. But you'll notice in the ODDSRATIO results that now the clinic effect depends on both sex and race. If you want clinic odds ratios for sex alone then you can ignore any variability over race. Similarly for the clinic odds ratio for race alone. That can be done using SLICE statements and the ODDSRATIO option. model SecondDose (event = "Yes") = CLINIC_TYPE|RECIP_SEX RECIP_AGE CLINIC_TYPE|RECIP_RACE_ETH; oddsratio CLINIC_TYPE; slice CLINIC_TYPE*RECIP_SEX / sliceby=RECIP_SEX diff cl oddsratio; slice CLINIC_TYPE*RECIP_RACE_ETH / sliceby=RECIP_RACE_ETH diff cl oddsratio; ... View more

I don't know what it was that did the trick, but something you mentioned below worked (lol). Thanks for your help! ... View more