Alt Ethan M, Psioda Matthew A, Ibrahim Joseph G
Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA.
Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC, 27599, USA.
Biostatistics. 2022 Dec 12;24(1):17-31. doi: 10.1093/biostatistics/kxab050.
In clinical trials, it is common to have multiple clinical outcomes (e.g., coprimary endpoints or a primary and multiple secondary endpoints). It is often desirable to establish efficacy in at least one of multiple clinical outcomes, which leads to a multiplicity problem. In the frequentist paradigm, the most popular methods to correct for multiplicity are typically conservative. Moreover, despite guidance from regulators, it is difficult to determine the sample size of a future study with multiple clinical outcomes. In this article, we introduce a Bayesian methodology for multiple testing that asymptotically guarantees type I error control. Using a seemingly unrelated regression model, correlations between outcomes are specifically modeled, which enables inference on the joint posterior distribution of the treatment effects. Simulation results suggest that the proposed Bayesian approach is more powerful than the method of Holm (1979), which is commonly utilized in practice as a more powerful alternative to the ubiquitous Bonferroni correction. We further develop multivariate probability of success, a Bayesian method to robustly determine sample size in the presence of multiple outcomes.
在临床试验中，有多个临床结局（例如，共同主要终点或一个主要终点和多个次要终点）是很常见的。通常希望在多个临床结局中的至少一个中确立疗效，这就导致了多重性问题。在频率主义范式中，最常用的校正多重性的方法通常较为保守。此外，尽管有监管机构的指导，但要确定未来具有多个临床结局的研究的样本量仍很困难。在本文中，我们介绍一种用于多重检验的贝叶斯方法，该方法渐近地保证了第一类错误控制。使用看似不相关的回归模型，对结局之间的相关性进行了专门建模，这使得能够对治疗效果的联合后验分布进行推断。模拟结果表明，所提出的贝叶斯方法比霍尔姆（1979年）的方法更具功效，霍尔姆方法在实践中通常被用作无处不在的邦费罗尼校正的更有效替代方法。我们进一步开发了多元成功概率，这是一种在存在多个结局的情况下稳健确定样本量的贝叶斯方法。
