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

频数推理 贝叶斯概率 等价(形式语言) 计量经济学 一致性(知识库) 计算机科学 数学 贝叶斯推理 统计 人工智能 离散数学
作者
Jos Weusten,Ji Young Kim,Katherine Giacoletti,Jorge Vázquez,Plinio De los Santos
出处
期刊:Journal of Applied Statistics [Taylor & Francis]
卷期号:: 1-20
标识
DOI:10.1080/02664763.2023.2297150
摘要

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.
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