I'm running a logistic regression model with main effects and two interaction terms. Both interaction terms in the model (ageten*education and ageten*homeown) were significant (p<0.05) and I wanted to have stratified odds ratios with one reference group (40-49 & post-secondary education, 40-49 & owned home) for each interaction. Code 1:  proc surveylogistic data=survey.family; /* model with both interactions */ weight wt; class ageten (ref='40-49') sex (ref='Male') state (ref='Florida') income (ref='Above LIM') homeown (ref='Owned') education (ref='Post-secondary') race (ref='White') immigration (ref='Non-immigrant')/ param = glm; model outcome(event='A') = ageten sex state income homeown education race immigration age*education age*homeown/ expb clodds nodummyprint; run; However, when I include expb in the code 1, I get odds ratios where the reference group is different for each stratum. For example, the reference group for '10-19 year old living in a rented home' is '10-19 year old living in owned home' and the reference group for '20-29 year old living in rented home' is '20-29 year old living in owned home'. I've also tried combining the variables involved in the interaction into a single variable (shown in code 2 below), however there seems to be problems with multicollinearity if age is included in both interactions. I was able to get the stratified odds with only one reference group in a model controlling for other sociodemographic characteristics, but I had to have a separate model for each interaction/combined variable. Also, I assume that doing it this way means I wouldn't be able to include the two individual variables in the model.  Code 2: /* Ageten_homeown interaction coding */ data survey.family; set survey.family; if ageten=4 and homeown=1 then agehome=0; else if ageten=1 and homeown=1 then agehome=1; else if ageten=2 and homeown=1 then agehome=2; else if ageten=3 and homeown=1 then agehome=3; else if ageten=1 and homeown=2 then agehome=4; else if ageten=2 and homeown=2 then agehome=5; else if ageten=3 and homeown=2 then agehome=6; else if ageten=4 and homeown=2 then agehome=7; run; proc format; value agehome 0='40-49 owned' 1='10-19 owned' 2='20-29 owned' 3='30-39 owned' 4= '10-19 rented' 5='20-29 rented' 6='30-39 rented' 7='40-49 rented'; run; proc surveylogistic data=survey.family; class  agehome (ref='40-49 owned') sex (ref='Male') state (ref='Florida') education (ref='Post-secondary') income (ref='Above LIM') race (ref='White') immigration (ref='Non-immigrant')/ param=ref; model outcome(event='A')= agehome sex state education income race immigration; weight wt; title 'Interaction ageten*homeownership on outcome A in adjusted model'; format agehome agehome.; run; What I would like to generate are odds ratios for each combination compared to one single reference group (like 40-49 year old living in owned home). I'm hoping to get a table of odds ratios that looks like this for both significant interaction terms included in the same model that's adjusted for other characteristics:                  Is there a way to produce odds ratios like that in the surveylogistic procedure? If not, is code 2 used for two models the best way to approach this? Thanks. ... View more