Bioequivalence and Bioavailability Forum

Hi John,

I don’t speak SAS, but until Detlew shows up you can try something: Instead of setting Test1’s observations to “.”, delete the respective rows completely.
Try in the last line to delete 0 from the ESTIMATE statement.

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Edit: PHX seems to be more tolerant. Same results whether I exclude one treatment (my standard), set them to “.”, or delete complete rows. In the second case it doesn’t matter whether I set one of the coefficients to 0 or leave it empty. Now for the interesting part: One of my studies, n=18, 6×3, all effects fixed.

Full model (df 30)
       PE      90% CI    ISCV   MSE
T1/R  98.91 94.73 103.27 7.42 0.005485
T2/R 101.59 97.29 106.07 7.42 0.005485

Crippled models (df 14 each)
       PE      90% CI    ISCV   MSE
T1/R  99.00 93.64 104.66 9.19 0.008413
T2/R 101.52 98.15 105.02 5.53 0.003108

CIs in the crippled models are wider or narrower due to the different pooled variances and dfs. The PEs are also slightly different (closer to 1), which was a little bit surprising to me.