I have a dataset (see attached), which basically involves 4 treatments for a chemotherapy drug. Samples were taken from 2 biopsy locations, and biopsy were taken at 2 time points. So biopsy location and time are nested within subject, and thus each subject has 4 data points (from 2 biopsy locations and 2 time points). The objective is to study treatment difference. time is 2-level factor (Day 4, Day 10) biopsysite is 2-level factor (S1 and S2) trt is 4-level factor (A, B, C, D) An exmple part of the dataet looks like: pid trt time biopsysite y age P2 A DAY 4 S1 -0.122 49 P2 A DAY 4 S2 -0.442 49 P2 A DAY 10 S1 0.229 49 P2 A DAY 10 S2 -0.007 49 P8 B DAY 4 S2 0.748 62 P8 B DAY 4 S1 0.086 62 P8 B DAY 10 S2 0.090 62 P8 B DAY 10 S1 0.076 62 : : : I would like to fit a mixed model with age, time, trt and time by trt as fixed factor. "time" as repeated measure using AR1(), and "biopsysite" as random with compound symmetry covariance structure between the 2 biopsy locations. How should I specify the mixed model? Is the following correct? proc mixed; class pid trt time biopsysite; model y = age trt time trt*time / s; random biopsysite / type=cs subject=pid g; repeated / type=ar(1) subject=pid*biopsysite r; lsmeans trt*time/cl pdiff; run; Or what about this one, does it make sense? proc mixed data=test; class time trt pid biopsysite; model y=age time trt time*trt/s; random biopsysite/type=cs g; repeated pid / type=ar(1) r; lsmeans trt*time / cl pdiff alpha=0.2; run;
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