University of Bremen, Institute for Statistics and Competence Center for Clinical Trials, Bremen, Germany.
University of Oldenburg, Institute of Mathematics, Oldenburg, Germany.
Stat Methods Med Res. 2023 Feb;32(2):334-352. doi: 10.1177/09622802221135249. Epub 2022 Dec 1.
We introduce a new multiple type I error criterion for clinical trials with multiple, overlapping populations. Such trials are of interest in precision medicine where the goal is to develop treatments that are targeted to specific sub-populations defined by genetic and/or clinical biomarkers. The new criterion is based on the observation that not all type I errors are relevant to all patients in the overall population. If disjoint sub-populations are considered, no multiplicity adjustment appears necessary, since a claim in one sub-population does not affect patients in the other ones. For intersecting sub-populations we suggest to control the average multiple type I error rate, i.e. the probability that a randomly selected patient will be exposed to an inefficient treatment. We call this the population-wise error rate, exemplify it by a number of examples and illustrate how to control it with an adjustment of critical boundaries or adjusted -values. We furthermore define corresponding simultaneous confidence intervals. We finally illustrate the power gain achieved by passing from family-wise to population-wise error rate control with two simple examples and a recently suggested multiple-testing approach for umbrella trials.
我们引入了一种新的多类型 I 错误标准，用于具有多个重叠人群的临床试验。这种试验在精准医学中很有意义，精准医学的目标是针对特定的亚人群（由遗传和/或临床生物标志物定义）开发靶向治疗方法。新的标准基于这样一种观察，即并非所有的 I 型错误都与总体人群中的所有患者相关。如果考虑不相交的亚人群，则不需要进行多重性调整，因为一个亚人群中的主张不会影响其他亚人群中的患者。对于相交的亚人群，我们建议控制平均多重 I 型错误率，即随机选择的患者接受无效治疗的概率。我们将其称为人群错误率，通过一些示例进行说明，并演示如何通过调整临界边界或调整值来控制它。我们还定义了相应的同时置信区间。最后，我们通过两个简单的例子和最近提出的伞式试验多重检验方法来说明从家族错误率控制到人群错误率控制所获得的功效增益。
