AI 中文总结
针对重叠人群临床试验的PWER缺陷，提出PWER-P与PWER-U两种改进方案，通过对比分析其I类错误控制与功效特性，为该类试验的错误率选择提供新方法。
AI 中文摘要
人群水平错误率（PWER）作为家族式错误率（FWER）的更宽松替代方案，被引入用于具有多个重叠患者人群的临床试验，这类试验在个性化医疗中尤为相关，个性化医疗旨在找到针对特定患者亚组的定制疗法。通过控制所有人群层的平均多重I类错误概率，PWER可大幅提高统计功效。然而，该概念的一个缺点是，特定人群的错误概率可能强烈依赖于分析中包含的其他人群的存在与否。为解决此问题，我们提出对PWER的两种修改方案，它们要么对所有目标人群实施个体错误控制，要么对所有目标人群的所有可能并集实施个体错误控制，我们将这些方法分别称为人群PWER（PWER-P）和人群并集PWER（PWER-U）。我们研究了这些新错误率的特性，并在I类错误控制和功效方面将它们与PWER和FWER进行了比较。
英文摘要
The population-wise error rate (PWER) was introduced as a more liberal alternative to the family-wise error rate (FWER) for clinical trials with multiple, overlapping patient populations. These trials are particularly relevant in personalized medicine, which aims to find therapies tailored to specific patient subgroups. By controlling an average multiple type I error probability over all population strata, the PWER can substantially improve statistical power. However, one disadvantage of this concept is that the error probability for a given population can strongly depend on the presence or absence of other populations included in the analysis. To address this issue, we propose two modifications of the PWER that enforce individual error control either for all target populations, or for all possible unions of target populations. We call these approaches the PWER over the populations (PWER-P) and the PWER over population unions (PWER-U). We investigate the properties of these new error rates and compare them with the PWER and FWER in terms of type I error control and power.
