Hello. I am running mixed models. The overall goal is to determine whether the changes in the outcomes differ by group and race. There are two racial groups, three treatment groups, one control group, and repeated measures. I have many estimate statements in the model. I am trying to determine whether there are differences in the means of the outcomes across the treatment groups and race. How do I determine the between-group differences (see below)?
Examples:
treatment 1 vs control
treatment 1 vs treatment 2
treatment 1 vs treatment 3
treatment 2 vs treatment 3
So on and so forth.......
treatment 1 vs control (race 1)
treatment 1 vs control (race 2)
treatment 1 vs treatment 2 (race 1)
treatment 1 vs treatment 2 (race 2)
So and so forth.........
treatment 1 vs control (race 1 vs race 2)
treatment 1 vs treatment 2 (race 1 vs race 2)
treatment 1 vs treatment 3 (race 1 vs race 2)
So on and so forth.......
treatment 1 vs control (race 1 vs race 2): baseline to 12 months
treatment 1 vs treatment 2 (race 1 vs race 2): baseline to 12 months
So on and so forth.......
Could you show the model you are currently using? I believe all of your questions can be resolved either by 'slicing' the LSMEANS or by writing appropriate LSMESTIMATE statements. It just would help to know what you are fitting before trying to write any code.
SteveDenham
Hi Steve,
Sure, thanks for your help. Please the model information below.
proc mixed data=work;
class id time group race ;
model outcome= time group race time*group race*group time*group*race;
Repeated time / Subject=ID type=cs;
lsmeans time*group time*group*race race*group /cl ;
-Renee
From your initial post, it appears to me that you want all possible comparisons within the various factors. Given that, consider this approach:
proc mixed data=work;
class id time group race ;
model outcome= time group race time*group race*group time*group*race;
Repeated time / Subject=ID type=cs;
lsmeans group time*group race*group
time*group*race /cl diff ;
The additions are in bold. One thing to consider is that you now have a lot of comparisons, and without some adjustment for this multiplicity, many of the "significant" comparisons are liable to be false positives. To handle this, read up on the adjust= option in the PROC MIXED documentation.
SteveDenham
Thank you. This is very helpful. I will try this approach and look into the adjust=option.
Are you ready for the spotlight? We're accepting content ideas for SAS Innovate 2025 to be held May 6-9 in Orlando, FL. The call is open until September 16. Read more here about why you should contribute and what is in it for you!
ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.
Find more tutorials on the SAS Users YouTube channel.