Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment
Optimizing randomized trial designs to distinguish which subpopulations benefit from treatment
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DOI：
10.1093/biomet/asr055
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发表时间：
2011-12-01
期刊：
影响因子：
2.7
通讯作者：
van der Laan, M. J.
中科院分区：
文献类型：
--
作者：
Rosenblum, M.;van der Laan, M. J.
在怀疑治疗效果可能仅对某些亚人群（例如那些疾病初始严重程度较高的人群，或具有特定基因变异的人群）较强的情况下，评估实验性治疗是一项挑战。在这种情况下，标准的随机对照试验可能效力较低。它们也没有经过优化以区分哪些亚人群从治疗中受益。为了克服这些局限性，我们考虑随机试验设计，其中患者入组标准可以根据期中分析以预先计划的方式改变。由于此类设计允许根据所纳入的人群进行依赖数据的改变，必须注意确保对总体I类错误率进行严格控制。我们的主要贡献是一种构建随机试验设计的通用方法，该方法允许使用预先指定的决策规则根据期中数据改变所纳入的人群，对于这种方法，渐近的总体I类错误率在指定的α水平上得到严格控制。作为我们方法的一个示例，我们为一种简单的两阶段富集设计证明了新的、精确的结果。然后，我们将这种设计与固定设计进行比较，重点关注每种设计确定总体和亚人群特定治疗效果的能力。
It is a challenge to evaluate experimental treatments where it is suspected that the treatment effect may only be strong for certain subpopulations, such as those having a high initial severity of disease, or those having a particular gene variant. Standard randomized controlled trials can have low power in such situations. They also are not optimized to distinguish which subpopulations benefit from a treatment. With the goal of overcoming these limitations, we consider randomized trial designs in which the criteria for patient enrollment may be changed, in a preplanned manner, based on interim analyses. Since such designs allow data-dependent changes to the population enrolled, care must be taken to ensure strong control of the familywise Type I error rate. Our main contribution is a general method for constructing randomized trial designs that allow changes to the population enrolled based on interim data using a prespecified decision rule, for which the asymptotic, familywise Type I error rate is strongly controlled at a specified level alpha. As a demonstration of our method, we prove new, sharp results for a simple, two-stage enrichment design. We then compare this design to fixed designs, focusing on each design's ability to determine the overall and subpopulation-specific treatment effects.