Bioequivalence and Bioavailability Forum

❝ Maybe this presentation helps. In short: Reference-scaling is based on the true population parameters (hence the Greek letters \(\theta_s,\,\mu_T,\,\mu_R,\,\sigma_{wR}\)). The true standard deviation \(\sigma_{wR}\) of the reference is unknown. We have only its estimate \(s_{wR}\) from the study. Imagine: The true within-subject CV of the reference is 27%. Hence, it is not an HVD(P) and we should use the conventional limits of 80.00-125.00%. However, by chance in our study we get an estimate of 35% and we expand the limits. Since the PE and the 90% are not affected it means that the chance of passing BE increases. The chance to falsely not accepting the Null increases and this is the inflated type I error.

Dear Helmut,
I may be wrong but I cannot see how we can get a true within-subject CV of any drug. I may be wrong, but even with simulations (I suppose with simulations some assumptions regarding variance should be made), it is very difficult or even impossible. Usually, we have very scarce data on within-subject CVs. How can we control in this situation TIE and what regulators say on this subject? I do not remember any reflection on this matter in official documents (EMA, FDA)?
Regards,
Mikalai