Statistics and Epidemiology, Division of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
Biometrics. 2023 Mar;79(1):9-19. doi: 10.1111/biom.13644. Epub 2022 Mar 25.
A popular design for clinical trials assessing targeted therapies is the two-stage adaptive enrichment design with recruitment in stage 2 limited to a biomarker-defined subgroup chosen based on data from stage 1. The data-dependent selection leads to statistical challenges if data from both stages are used to draw inference on treatment effects in the selected subgroup. If subgroups considered are nested, as when defined by a continuous biomarker, treatment effect estimates in different subgroups follow the same distribution as estimates in a group-sequential trial. This result is used to obtain tests controlling the familywise type I error rate (FWER) for six simple subgroup selection rules, one of which also controls the FWER for any selection rule. Two approaches are proposed: one based on multivariate normal distributions suitable if the number of possible subgroups, k, is small, and one based on Brownian motion approximations suitable for large k. The methods, applicable in the wide range of settings with asymptotically normal test statistics, are illustrated using survival data from a breast cancer trial.
一种常用于评估靶向治疗的临床试验设计是两阶段自适应富集设计，第二阶段的招募仅限于根据第一阶段的数据确定的生物标志物定义亚组。如果使用两阶段的数据来推断所选亚组中的治疗效果，那么数据依赖性选择会带来统计挑战。如果考虑的亚组是嵌套的，例如，由连续生物标志物定义的亚组，那么不同亚组中的治疗效果估计与群组序贯试验中的估计具有相同的分布。该结果用于获得六种简单亚组选择规则的检验，其中一个规则还控制任何选择规则的 FWER。提出了两种方法：一种基于多元正态分布，适用于可能的亚组数量 k 较小的情况，另一种基于布朗运动逼近，适用于 k 较大的情况。这些方法适用于具有渐近正态检验统计量的广泛环境，使用乳腺癌试验的生存数据进行了说明。
