A Bayesian approach for evaluating equivalence over multiple groups, and comparison with frequentist tost

Jos Weusten, Ji Young Kim, Katherine Giacoletti, Jorge Vázquez and Plinio De los Santos

Journal of Applied Statistics, 2024, vol. 51, issue 12, 2382-2401

Abstract: Manufacturing and testing of pharmaceutical products frequently occur in multiple facilities within a company’s network. It is of interest to demonstrate equivalence among the alternative testing/manufacturing facilities to ensure product consistency and quality regardless of the facility where it was manufactured/tested. In the Frequentist framework, equivalence testing is well established when comparing two labs or manufacturing facilities; however, when considering more than two labs or production sites, the Frequentist approach may not always offer appropriate or interpretable estimates for demonstrating equivalence among all of them simultaneously. This paper demonstrates the utility of Bayesian methods to the equivalence assessment of multiple groups means, with a comparison against traditional Frequentist methods. We conclude that a Bayesian strategy is very useful for addressing the problem of multi-group equivalence. While it is not our intention to argue that Bayesian methods should always replace Frequentist ones, we show that among the advantages of a Bayesian analysis is that it provides a more nuanced understanding of the degree of similarity among sites than the hypothesis testing underpinning the Frequentist approach.

Date: 2024
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DOI: 10.1080/02664763.2023.2297150

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