🤖 AI Summary
This study addresses the vulnerability of traditional population-wise error rate (PWER) methods in overlapping subgroup clinical trials for personalized medicine, where Type I error control for a specific subgroup can be adversely affected by other subgroups. To overcome this limitation, the authors propose two novel multiple error rate control procedures—PWER-P and PWER-U—that enforce individualized error rate control over all target subgroups and any arbitrary union of them, respectively. Built upon probability theory and multiple hypothesis testing principles, the proposed framework rigorously controls the Type I error while substantially enhancing statistical power. Compared with conventional PWER and family-wise error rate (FWER) approaches, these new methods demonstrate superior stability, controllability, and practical utility in settings involving overlapping subpopulations.
📝 Abstract
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.