Zhang Zhiwei, Chen Ruizhe, Soon Guoxing, Zhang Hui
Department of Statistics, University of California at Riverside, Riverside, California, USA.
Division of Epidemiology and Biostatistics, School of Public Health, University of Illinois at Chicago, Chicago, Illinois, USA.
Stat Med. 2018 Jan 15;37(1):1-11. doi: 10.1002/sim.7497. Epub 2017 Sep 26.
Adaptive enrichment designs (AEDs) of clinical trials allow investigators to restrict enrollment to a promising subgroup based on an interim analysis. Most of the existing AEDs deal with a small number of predefined subgroups, which are often unknown at the design stage. The newly developed Simon design offers a great deal of flexibility in subgroup selection (without requiring pre-defined subgroups) but does not provide a procedure for estimating and testing treatment efficacy for the selected subgroup. This article proposes a 2-stage AED which does not require predefined subgroups but requires a prespecified algorithm for choosing a subgroup on the basis of baseline covariate information. Having a prespecified algorithm for subgroup selection makes it possible to use cross-validation and bootstrap methods to correct for the resubstitution bias in estimating treatment efficacy for the selected subgroup. The methods are evaluated and compared in a simulation study mimicking actual clinical trials of human immunodeficiency virus infection.
临床试验的适应性富集设计（AEDs）允许研究者根据中期分析将入组限制在一个有前景的亚组。现有的大多数AEDs处理少量预先定义的亚组，而这些亚组在设计阶段通常是未知的。新开发的西蒙设计在亚组选择方面提供了很大的灵活性（无需预先定义亚组），但没有提供估计和检验所选亚组治疗效果的程序。本文提出了一种两阶段AED，它不需要预先定义亚组，但需要一个基于基线协变量信息选择亚组的预先指定算法。有一个预先指定的亚组选择算法使得使用交叉验证和自助法来校正估计所选亚组治疗效果时的重代入偏差成为可能。在一项模拟人类免疫缺陷病毒感染实际临床试验的模拟研究中对这些方法进行了评估和比较。
