Efficient model-based bioequivalence testing

Möllenhoff, K. and Loingeville, F. and Bertrand, J. and Nguyen, T.T. and Sharan, S. and Zhao, L. and Fang, L. and Sun, G. and Grosser, S. and Mentré, F. and Dette, H.

Volume: 23 Pages: 314-327
DOI: 10.1093/biostatistics/kxaa026
Published: 2022

The classical approach to analyze pharmacokinetic (PK) data in bioequivalence studies aiming to compare two different formulations is to perform noncompartmental analysis (NCA) followed by two one-sided tests (TOST). In this regard, the PK parameters area under the curve (AUC) and $C_{\max}$ are obtained for both treatment groups and their geometric mean ratios are considered. According to current guidelines by the U.S. Food and Drug Administration and the European Medicines Agency, the formulations are declared to be sufficiently similar if the $90\%$ confidence interval for these ratios falls between $0.8$ and $1.25 $. As NCA is not a reliable approach in case of sparse designs, a model-based alternative has already been proposed for the estimation of $\rm AUC$ and $C_{\max}$ using nonlinear mixed effects models. Here we propose another, more powerful test than the TOST and demonstrate its superiority through a simulation study both for NCA and model-based approaches. For products with high variability on PK parameters, this method appears to have closer type I errors to the conventionally accepted significance level of $0.05$, suggesting its potential use in situations where conventional bioequivalence analysis is not applicable. © The Author 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail:

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