Alosh Mohamed, Huque Mohammad F
Division of Biometrics III, Office of Biostatistics, OTS, CDER, FDA, 10903 New Hampshire Avenue, Silver Spring, MD 20993, USA.
Stat Med. 2009 Jan 15;28(1):3-23. doi: 10.1002/sim.3461.
Subgroup analyses in addition to the total study population analysis are common in clinical trials. However, it is well recognized that findings from subgroup analyses do not provide confirmatory evidence for subgroup treatment effects without placing a priori criteria for ensuring that their findings are scientifically sound. In this paper we address some of the common pitfalls of subgroup analyses. Subgroups analyses inherently have low power for detecting treatment effects. We investigate the power interplay for a subgroup analysis and that for the total study population and list factors that impact the power of a subgroup analysis. Then we introduce a flexible statistical strategy for testing a pre-specified sequence of hypotheses for both the overall and a subgroup. The proposed method strongly controls the familywise Type I error rate and enjoys higher power than other traditional methods. This testing strategy allows testing for a subgroup once a pre-specified degree of consistency in the efficacy findings between the subgroup and the overall study population is met. In addition, it accounts for the dependency between test statistics for the subgroup and the overall study population. We discuss the power performance of this new method and provide significance levels for subgroup analysis. Finally, we illustrate its application through retrospective analysis of data from three published clinical trials.
除了对整个研究人群进行分析外，亚组分析在临床试验中也很常见。然而，人们普遍认识到，亚组分析的结果如果没有事先设定确保其结果科学合理的标准，就不能为亚组治疗效果提供确证性证据。在本文中，我们探讨了亚组分析中一些常见的陷阱。亚组分析本身检测治疗效果的效能较低。我们研究了亚组分析与整个研究人群分析的效能相互作用，并列出了影响亚组分析效能的因素。然后，我们引入一种灵活的统计策略，用于检验总体和亚组预先指定的假设序列。所提出的方法严格控制了家族性I型错误率，并且比其他传统方法具有更高的效能。这种检验策略允许在亚组与整个研究人群的疗效结果达到预先指定的一致程度后，对亚组进行检验。此外，它考虑了亚组和整个研究人群的检验统计量之间的依赖性。我们讨论了这种新方法的效能表现，并提供了亚组分析的显著性水平。最后，我们通过对三项已发表的临床试验数据进行回顾性分析来说明其应用。
