Scaled average bioequivalence methods for highly variable drugs: Leveling‐off soft limits and the EMA's 2010 guideline (some ways to improve its type I error control)

• Published in: Statistics in medicine Vol. 43; no. 7; pp. 1475 - 1488
• Main Authors: Muñoz, Joel, Ocaña, Jordi, Suárez, Rolando, Millapán, Carolina
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 30.03.2024; Wiley Subscription Services, Inc
• Subjects: Bioequivalence; interval inclusion principle; Pharmacokinetics; Pharmacology; point estimate constraint; scaled average bioequivalence; point estimate constraint; interval inclusion principle; scaled average bioequivalence
• ISSN: 0277-6715; 1097-0258
• DOI: 10.1002/sim.10021

The regulatory EMA's reference scaled average bioequivalence (RSABE) approach for highly variable drugs suffers from some type I error control problems at the neighborhood of the 30% coefficient of variation (CV), where the bioequivalence (BE) limits change from constant to linearly scaled. This paper analyses BE inference methods based on the “Leveling‐off” (LO) soft sigmoid expanding BE limits that were proposed as an appealing surrogate for the EMA's limits and compares both approaches, on the replicated and partially replicated crossover designs. The initially proposed version of the LO method also has type I error inflation problems, albeit attenuated. But given its more mathematically regular character, it is more suitable for analytical corrections. Here we introduce two improvements over LO, one based on the application of Howe's method and the other based on correcting the estimation error. They further reduce the type I error inflation, although it does not disappear completely. Finally, the effect of heteroscedasticity on the above results is studied. It leads to inflation or deflation of the type I error, depending on the design and the type of heteroscedasticity (variability of the test product greater than that of the reference product or the opposite). The replicated design is much more stable against these effects than the partially replicated design and maintains these improvements much better.