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posted by d_labes – Berlin, Germany, 2011-05-03 16:32 (5522 d 23:12 ago) – Posting: # 6984
Views: 13,383

Dear randombadger,

that results show for me:

The FDA model (model 2), not making any assumptions about equal variances, gives you somewhat different inter-subject variabilities (CV_bA=33%, CV_bB=27%) plus a non-negligible subject-by-formulation interaction
s²_D = g₁₁+g₂₂-2*g₁₂ = 0.0591 s_D = 0.2431
Remember s_D>0.15 for log-transformed values was considered as big in the old days of IBE.
The constraint model 1 (assuming s²_D = 0 which lead to equal inter-subject variabilities) must absorb these inter-subject variance-covariance terms into the other random terms, namely the intra-subject (residual) variances attributed to the treatments A or B. Therefore the intra-subject CV especially for treatment A rises.
I bet some EURO cent that the model 2 fits your data better. Have a look at the Akaike information criterion (AIC) or Bayesian information criterion (BIC) under Fit statistics to verify this. These information criteria (there are some other in the SAS output if you run your Proc MIXED code with the option IC) you can use to choose your model.
If we interpret treatment A for a moment as Reference the differences in the intra-subject CVs between both models can make the step from not allowed to allowed scaled, widened acceptance ranges (sABE). Since the constraint model is a step from the FDA model to the EMA Q&A recommended evaluation we could be happy for our sponsors .

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Regards,

Detlew