Model‐based bioequivalence approach for sparse pharmacokinetic bioequivalence studies: Model selection or model averaging?

• Published in: Statistics in medicine 2024-08, Vol.43 (18), p.3403-3416
• Main Authors: Philipp, Morgane, Tessier, Adrien, Donnelly, Mark, Fang, Lanyan, Feng, Kairui, Zhao, Liang, Grosser, Stella, Sun, Guoying, Sun, Wanjie, Mentré, France, Bertrand, Julie
• Format: Article
• Language: English
• Subjects: Bioequivalence; Computer Simulation; Cross-Over Studies; Humans; Hypotheses; Life Sciences; model averaging; model selection; Models, Statistical; non‐linear mixed effect models; Pharmaceutical sciences; Pharmacokinetics; Pharmacology; Simulation; Statistics; Therapeutic Equivalency; two one‐sided test
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.10088

Conventional pharmacokinetic (PK) bioequivalence (BE) studies aim to compare the rate and extent of drug absorption from a test (T) and reference (R) product using non‐compartmental analysis (NCA) and the two one‐sided test (TOST). Recently published regulatory guidance recommends alternative model‐based (MB) approaches for BE assessment when NCA is challenging, as for long‐acting injectables and products which require sparse PK sampling. However, our previous research on MB‐TOST approaches showed that model misspecification can lead to inflated type I error. The objective of this research was to compare the performance of model selection (MS) on R product arm data and model averaging (MA) from a pool of candidate structural PK models in MBBE studies with sparse sampling. Our simulation study was inspired by a real case BE study using a two‐way crossover design. PK data were simulated using three structural models under the null hypothesis and one model under the alternative hypothesis. MB‐TOST was applied either using each of the five candidate models or following MS and MA with or without the simulated model in the pool. Assuming T and R have the same PK model, our simulation shows that following MS and MA, MB‐TOST controls type I error rates at or below 0.05 and attains similar or even higher power than when using the simulated model. Thus, we propose to use MS prior to MB‐TOST for BE studies with sparse PK sampling and to consider MA when candidate models have similar Akaike information criterion.