Rosenblum Michael, Qian Tianchen, Du Yu, Qiu Huitong, Fisher Aaron
Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA
Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA.
Biostatistics. 2016 Oct;17(4):650-62. doi: 10.1093/biostatistics/kxw014. Epub 2016 Mar 22.
Adaptive enrichment designs involve preplanned rules for modifying enrollment criteria based on accrued data in an ongoing trial. For example, enrollment of a subpopulation where there is sufficient evidence of treatment efficacy, futility, or harm could be stopped, while enrollment for the remaining subpopulations is continued. We propose a new class of multiple testing procedures tailored to adaptive enrichment designs. The procedures synthesize ideas from two general approaches. As in the modified group sequential approach, the procedures gain power by leveraging the covariance among statistics for different stages and different hypotheses. As in the alpha reallocation approach, the procedures lower rejection thresholds for the remaining null hypotheses after others have been rejected. The proposed procedures are proved to have power greater than or equal to several existing methods, and to strongly control the familywise Type I error rate when statistics are normally distributed. The methods are illustrated through simulations of a trial for a surgical intervention for stroke, involving two subpopulations.
适应性富集设计涉及基于正在进行的试验中累积的数据来修改纳入标准的预先规划规则。例如,对于有足够证据证明治疗有效、无效或有害的亚组,可以停止入组,而其余亚组的入组继续进行。我们提出了一类专门针对适应性富集设计的新的多重检验程序。这些程序综合了两种一般方法的思想。与修正的组序贯方法一样,这些程序通过利用不同阶段和不同假设的统计量之间的协方差来提高检验效能。与α重新分配方法一样,在其他原假设被拒绝后,这些程序会降低其余原假设的拒绝阈值。所提出的程序被证明具有大于或等于几种现有方法的检验效能,并且在统计量呈正态分布时能严格控制家族性Ⅰ型错误率。通过对一项涉及两个亚组的中风手术干预试验的模拟来说明这些方法。