Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
Clin Trials. 2022 Oct;19(5):512-521. doi: 10.1177/17407745221095855. Epub 2022 May 9.
BACKGROUND/AIMS: Secondary analyses of randomized clinical trials often seek to identify subgroups with differential treatment effects. These discoveries can help guide individual treatment decisions based on patient characteristics and identify populations for which additional treatments are needed. Traditional analyses require researchers to pre-specify potential subgroups to reduce the risk of reporting spurious results. There is a need for methods that can detect such subgroups without specification while allowing researchers to control the probability of falsely detecting heterogeneous subgroups when treatment effects are uniform across the study population.
We propose a permutation procedure for tuning parameter selection that allows for type I error control when testing for heterogeneous treatment effects framed within the Virtual Twins procedure for subgroup identification. We verify that the type I error rate can be controlled at the nominal rate and investigate the power for detecting heterogeneous effects when present through extensive simulation studies. We apply our method to a secondary analysis of data from a randomized trial of very low nicotine content cigarettes.
In the absence of type I error control, the observed type I error rate for Virtual Twins was between 99% and 100%. In contrast, models tuned via the proposed permutation were able to control the type I error rate and detect heterogeneous effects when present. An application of our approach to a recently completed trial of very low nicotine content cigarettes identified several variables with potentially heterogeneous treatment effects.
The proposed permutation procedure allows researchers to engage in secondary analyses of clinical trials for treatment effect heterogeneity while maintaining the type I error rate without pre-specifying subgroups.
背景/目的：随机临床试验的二次分析通常旨在确定具有不同治疗效果的亚组。这些发现可以帮助根据患者特征指导个体化治疗决策，并确定需要额外治疗的人群。传统分析要求研究人员预先指定潜在的亚组，以降低报告虚假结果的风险。需要一种无需指定即可检测此类亚组的方法，同时允许研究人员在治疗效果在研究人群中均匀分布时控制错误地检测到异质亚组的概率。
我们提出了一种用于调整参数选择的置换程序，允许在虚拟双胞胎（用于亚组识别的程序）框架内测试异质治疗效果时控制Ⅰ型错误率。我们验证了可以在名义速率下控制Ⅰ型错误率，并通过广泛的模拟研究调查了当存在异质效应时检测异质效应的功效。我们将我们的方法应用于对极低尼古丁含量香烟的随机试验数据的二次分析。
在没有Ⅰ型错误控制的情况下，虚拟双胞胎的观察到的Ⅰ型错误率在 99%到 100%之间。相比之下，通过提议的置换调整的模型能够控制Ⅰ型错误率并检测到存在的异质效应。我们的方法在最近完成的极低尼古丁含量香烟试验中的应用确定了几个具有潜在异质治疗效果的变量。
所提出的置换程序允许研究人员在不预先指定亚组的情况下，对临床试验的治疗效果异质性进行二次分析，同时保持Ⅰ型错误率。
