Mathematics and Computer Science, Competence Center for Clinical Trials Bremen, University of Bremen, Bremen, Germany.
Biometrics. 2023 Dec;79(4):2806-2810. doi: 10.1111/biom.13909. Epub 2023 Jul 17.
This comment builds on the familywise expected loss (FWEL) framework suggested by Maurer, Bretz, and Xun in 2022. By representing the populationwise error rate (PWER) as FWEL, it is illustrated how the FWEL framework can be extended to clinical trials with multiple and overlapping populations and the PWER can be generalized to more general losses. The comment also addresses the question of how to deal with midtrial changes in the posttrial risks and related losses that are caused by data-driven decisions. Focusing on multiarm trials with the possibility of dropping treatments midtrial, we suggest to switch from control of the unconditional expected loss to control of the conditional expected loss that is related to the actual risks and is conditional on the sample event that causes the change in the risks. The problem and here suggested solution is also motivated with a sequence of independent trials for a hitherto incurable disease which ends when an efficient treatment is found. No multiplicity adjustment is applied in this case and we show how this can be justified by the consideration of the changing out-trial risks and with control of conditional type I error rates and losses.
本评论以 Maurer、Bretz 和 Xun 于 2022 年提出的总体预期损失 (FWEL) 框架为基础。通过将总体误报率 (PWER) 表示为 FWEL,说明了如何将 FWEL 框架扩展到具有多个重叠人群的临床试验中,以及如何将 PWER 推广到更一般的损失。该评论还解决了如何处理因数据驱动决策而导致的试验后风险和相关损失在试验期间发生变化的问题。本文重点讨论了具有中途停止治疗可能性的多臂试验,建议从控制无条件预期损失切换到控制与实际风险相关的条件预期损失,该条件预期损失取决于导致风险变化的样本事件。该问题和这里提出的解决方案也受到了一个迄今为止无法治愈的疾病的独立试验序列的启发,当找到有效的治疗方法时,该序列就会结束。在这种情况下不进行多重性调整,我们通过考虑试验后风险的变化以及控制条件型 I 类错误率和损失,展示了如何对此进行证明。