Bayesian and Frequentist Approaches to Rescuing Disrupted Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions
Bayesian and Frequentist Approaches to Rescuing Disrupted Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions
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DOI：
10.1080/19466315.2024.2313986
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发表时间：
2024-03-14
影响因子：
1.8
通讯作者：
Flournoy，Nancy
中科院分区：
文献类型：
--
作者：
Kunz，Cornelia Ursula;Tarima，Sergey;Flournoy，Nancy
COVID-19大流行以前所未有的方式影响了临床试验。然而，今后应该会遇到类似的挑战。这些挑战使我们认识到，其他类型的干扰所产生的统计问题以前没有引起统计界的注意。本文介绍了一些频率论和贝叶斯统计工具，可以用于未来的中断和照明的问题，可以受益于更多的统计研究。干扰可能会威胁到临床试验的有效性。在这里，我们解决了两个由此产生的挑战：（a）进行计划外分析，选择停止和/或改变样本量;（B）在患者水平上可观察或不可观察的研究人群变化。不同的范式导致不同的做事方式，但许多统计学家完全在贝叶斯或频率论范式中工作。我们提出并提供并排描述贝叶斯和频率论的方法来处理这些挑战。一项说明性的III期试验旨在比较2型糖尿病的二线治疗。假设试验因COVID-19中断，我们比较和对比贝叶斯和频率主义应对策略，重点关注I型错误控制和特定效用函数的预期损失。
The COVID-19 pandemic impacted clinical trials in ways never expected. However, similar challenges should now be expected going forward. These challenges made us aware of statistical problems arising from other types of disruptions that had not previously captured the attention of the statistical community. This article describes some frequentist and Bayesian statistical tools that can be used with future disruptions and illuminates issues that could benefit from more statistical research. Disruptions may threaten a clinical trial’s validity. Here, we address two resultant challenges: (a) performing an unplanned analysis with options to stop and/or change the sample size; and (b) changes in the study population that are observable or unobservable at the patient level. Different paradigms lead to different ways of doing things, but many statisticians work exclusively within a Bayesian or frequentist paradigm. We propose and provide side-by-side descriptions of Bayesian and frequentist approaches to dealing with these challenges. An illustrative phase III trial aims to compare second-line therapies for type 2 diabetes. We compare and contrast Bayesian and frequentist coping strategies assuming the trial was interrupted due to COVID-19, focusing on Type I error control and the expected loss from a specific utility function.