A well-known issue when testing for treatment-by-subgroup interaction is its low power, as clinical trials are generally powered for establishing efficacy claims for the overall population, and they are usually not adequately powered for detecting interaction (Alosh, Huque, & Koch [2015] Journal of Biopharmaceutical Statistics, 25, 1161-1178). Hence, it is necessary to develop an adaptive design to improve the efficiency of detecting heterogeneous treatment effects within subgroups. Considering Neyman allocation can maximize the power of usual Z-test (see p. 194 of the book edited by Rosenberger and Lachin), we propose a subgroup-adaptive randomization procedure aiming to achieve Neyman allocation in both predefined subgroups and overall study population in this paper. To verify whether the proposed randomization procedure works as intended, relevant theoretical results are derived and displayed . Numerical studies show that the proposed randomization procedure has obvious advantages in power of tests compared with complete randomization and Pocock and Simon's minimization method.

中文翻译：

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临床试验中亚组确认的亚组适应性随机化

测试亚组治疗相互作用时的一个众所周知的问题是它的低功效，因为临床试验通常能够为总体人群建立疗效声明，并且它们通常没有足够的功效来检测相互作用（Alosh，Huque，＆amp; Koch [2015] 生物制药统计杂志，25, 1161-1178)。因此，有必要开发一种自适应设计来提高检测亚组内异质治疗效果的效率。考虑到 Neyman 分配可以最大化常规 Z 检验的能力（参见 Rosenberger 和 Lachin 编辑的书的第 194 页），我们提出了一种亚组自适应随机化程序，旨在在预定义的亚组和整个研究人群中实现 Neyman 分配纸。为了验证提议的随机化程序是否按预期工作，推导并展示了相关的理论结果。数值研究表明，与完全随机化和Pocock和Simon最小化方法相比，所提出的随机化程序在检验能力方面具有明显优势。