There is one issue that you should think about some more, in my opinion. It looks like you are "adjusting" for baseline twice - once by calculating the percent change for the given subject, and again by using the baseline as a covariate.  If you have the percentage change for each subject, then I would remove the baseline covariate from the model.    SteveDenham ... View more

proc glm data=have; class group covA covB; model response = covA covB covC baseline group group*baseline; lsmeans group/pdiff; run; @Rhea1234, this code should help you accommodate the things brought up in your private message.  At this point, I would recommend responding here rather than be a private message - there are other folks who can help.   The code above will give the difference between groups, averaged over the categorical effects covA and covB,, and estimated at the mean values of the continuous effects covC and baseline.  The F test for group*baseline will tell you whether the adjustment is the same in both groups.  Now I'll recommend getting a good text on analysis of covariance to see how to proceed.  If you have access to SAS for Mixed Models, there is a great chapter that goes through this step by step, and how to get the comparisons needed if the adjustments for baseline are not equal.   SteveDenham ... View more

Here is how I would approach this.  There are two levels of group (say A and B) and two levels of time (t0 and t+2). I would use PROC MIXED as you have correlated responses. Follow this up with a post-analysis processing to get the percent change.  Something like this:   proc mixed data=have; class group time subject; /* subject is a member of one of the levels of group */ model response = group time group*time; repeated time/type=un subject=subject; lsmestimate group*time 'Baseline Group A' 1 0 0 0; lsmestimate group*time 'Baseline Group B' 0 0 1 0; lsmestimate group*time 'Difference over time for Group A' 1 -1 0 0; lsmestimate group*time 'Difference over time for Group B' 0 0 1 -1; lsmestimate group*time 'Difference between groups over time (B - A)' -1 1 1 -1; ods output lsmestimates=lsmestimates; run; Then you can calculate the percent change in each group by dividing the difference over time by the baseline for that group.  The p value from the last lsmestimate would apply to the difference in group changes. No place along here would there be an analysis of percent change.   I strongly recommend against analyzing the percent changes as a response variable.  See Harrell's Applied Regression Strategies or the website run by the Vanderbilt University biostats department https://discourse.datamethods.org/t/author-checklist/3407 .  In particular, see this webpage:  http://hbiostat.org/bbr/md/change.html#sec:changegen  for the many caveats on analyzing change scores.   SteveDenham ... View more

Is your dataset so large that you have to use HPGENSELECT, rather than GLMSELECT?  Because if you can use the latter to do the LASSO selection, you have access to the STORE statement, from which you can use PLM to get least squares means and odds ratios.   SteveDenham ... View more