Bayesian and Frequentist Approaches to Rescuing Disrupted Trials: A Report from the NISS Ingram Olkin Forum Series on Unplanned Clinical Trial Disruptions

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.

新冠疫情（COVID-19）以超乎预期的方式对临床试验产生了冲击，且未来这类挑战或将成为需要应对的常态。此类挑战也让我们意识到，其他类型的试验中断所引发的统计学问题，此前并未得到统计学界的足够关注。本文介绍了若干可用于应对未来试验中断场景的频率学派（frequentist）与贝叶斯学派（Bayesian）统计学工具，并阐明了有待通过更多统计学研究加以完善的相关问题。试验中断可能会危及临床试验的有效性。针对由此衍生的两项挑战，本文展开探讨：(a) 开展非计划性分析，并可选择提前终止试验或调整样本量；(b) 研究人群在患者层面出现可观测或不可观测的变化。不同的统计范式会催生不同的研究路径，而多数统计学家仅在贝叶斯或频率学派范式下开展工作。针对上述挑战，本文提出并对比阐述了贝叶斯与频率学派的应对方案。本文以一项旨在对比2型糖尿病（type 2 diabetes）二线治疗方案的示范性III期临床试验（phase III trial）为案例，假设该试验因新冠疫情（COVID-19）中断，对比分析贝叶斯与频率学派的应对策略，重点关注第一类错误（Type I error）控制与特定效用函数（utility function）下的期望损失。