This document discusses interpreting output from the PROC MIXED procedure in SAS. It explains that PROC MIXED can be used to fit fixed and random effect models for repeated measures data. The code provided runs PROC MIXED with a class statement to define factors, a model statement to specify the fixed effects, repeated statement for the random effect of subject, and LSMEANS statement to obtain least squares means and differences between means. The conclusion drawn is that treatments AA11 and placebo were found to work equally based on the PROC MIXED analysis.

1. Interpretation of PROC MIXED Output
Difference of least square means
Least square Mean
Type III fixed effects
Data Snapshot
proc mixed data=sasdata order=data;
class pt trtp avisit;
model chg = base trtp avisit trtp*avisit/solution;
repeated avisit /type=un subject=pt;
lsmeans trtp*avisit / pdiff cl;
ods output tests3=test3 lsmeans = lsmeans diffs =
diffs;
run;
Code
Proc
Mixed
Fit Fixed & Random
effect model
Repeated Measures
Desired output
 To decide which values
should be considered for
desired output from different
part of Proc Mixed outputs .
 Read and interpret values
from Proc Mixed output.
Conclusion :
Treatments AA11 and Placebo
work equally.