Bioequivalence and Bioavailability Forum

Dear Detlew,

❝ I'm not a friend of this crippled approach .

Me not either. On the other hand, remember your own simulations?

❝ On the other hand the full analysis is crucially depending on the assumption of a common error variance for the three treatments. What happens and what are the implications if this assumption is violated is not really clear to me.

❝

❝ Is the case of heteroscedastic variances in BE studies an issue? Don't ask me . See a quote of Steven Senn here.

Stephen and Andy were taking about study planning and IMHO, they erred. We all know that f.i. generic formulations of PPIs show lower CVs since many references are lousy pro­ducts (mainly problems with occasionally failing gastro­resistant coating). Should we not perform such studies because we expect hetero­scedasticity? Doesn’t make sense to me. Although the author of famous PowerTOST is not entirely happy with the underlying maths, the outcome (assuming different CV_Ws in replicate designs) matches common sense: A reward in terms of power / sample size if CV_WT < CV_WR and a penalty if CV_WT > CV_WR.

Another story are parallel designs ( ElMaestro). Here we deal with the pooled total variance, which will be influenced by different formulation CVs as well, but to a much lesser extent than in Xovers. Differences between groups end up straight in the treatment com­parison. Therefore, it is of paramount importance to standardize studies in a parallel design. Though matched pairs are sometimes used in phase III I don’t know whether such an approach was ever seriously* tried in BE. It would need some a priori knowledge of influential covariates on PK. BMI, body-weight and/or -fat, sex, and renal clearance should perform better than eye’s color.

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• Well, I tried in the last century, but the sample size was too small for meaningful matching. I ran out of degrees of freedom. Was just some kind of post hoc hobby project.