United Therapeutics Corp., Research Triangle Park, Durham, North Carolina, USA.
Department of Statistics, North Carolina State University, Raleigh, North Carolina, USA.
Biometrics. 2023 Sep;79(3):2116-2126. doi: 10.1111/biom.13711. Epub 2022 Aug 2.
Recent statistical methodology for precision medicine has focused on either identification of subgroups with enhanced treatment effects or estimating optimal treatment decision rules so that treatment is allocated in a way that maximizes, on average, predefined patient outcomes. Less attention has been given to subgroup testing, which involves evaluation of whether at least a subgroup of the population benefits from an investigative treatment, compared to some control or standard of care. In this work, we propose a general framework for testing for the existence of a subgroup with enhanced treatment effects based on the difference of the estimated value functions under an estimated optimal treatment regime and a fixed regime that assigns everyone to the same treatment. Our proposed test does not require specification of the parametric form of the subgroup and allows heterogeneous treatment effects within the subgroup. The test applies to cases when the outcome of interest is either a time-to-event or a (uncensored) scalar, and is valid at the exceptional law. To demonstrate the empirical performance of the proposed test, we study the type I error and power of the test statistics in simulations and also apply our test to data from a Phase III trial in patients with hematological malignancies.
近年来，精准医学的统计方法主要集中在识别具有增强治疗效果的亚组或估计最佳治疗决策规则，以便以平均方式最大化预定的患者结果来分配治疗。对亚组检验的关注较少，亚组检验涉及评估与某些对照或标准治疗相比，人群中至少是否有一个亚组受益于研究性治疗。在这项工作中，我们提出了一种基于估计最优治疗方案下估计的价值函数与将每个人分配到相同治疗方案的固定方案下的差值来检验增强治疗效果的亚组存在的一般框架。我们提出的检验不需要指定亚组的参数形式，并允许亚组内存在异质的治疗效果。该检验适用于感兴趣的结果是事件时间或（未删失）标量的情况，并且在例外律下有效。为了展示所提出检验的经验性能，我们在模拟中研究了检验统计量的Ⅰ类错误和功效，并且还将我们的检验应用于血液恶性肿瘤患者的 III 期临床试验数据。
