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一种用于评估多组间等效性的贝叶斯方法,以及与频率主义者非劣效性检验的比较。

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

作者信息

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

机构信息

Center for Mathematical Sciences, MSD, Oss, The Netherlands.

Center for Mathematical Sciences, Merck & Co., Inc., Rahway, NJ, USA.

出版信息

J Appl Stat. 2024 Jan 12;51(12):2382-2401. doi: 10.1080/02664763.2023.2297150. eCollection 2024.

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.

摘要

药品的生产和测试通常在公司网络内的多个设施中进行。证明替代测试/生产设施之间的等效性很有意义,以确保产品的一致性和质量,而不管其生产/测试的设施如何。在频率学派框架中,在比较两个实验室或生产设施时,等效性测试已经很成熟;然而,当考虑两个以上的实验室或生产地点时,频率学派方法可能并不总是能提供合适的或可解释的估计,以同时证明它们之间的等效性。本文展示了贝叶斯方法在多组均值等效性评估中的效用,并与传统的频率学派方法进行了比较。我们得出结论,贝叶斯策略对于解决多组等效性问题非常有用。虽然我们并非主张贝叶斯方法应始终取代频率学派方法,但我们表明,贝叶斯分析的优点之一是,与频率学派方法所基于的假设检验相比,它能更细致地理解各地点之间的相似程度。

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