Kovalchik Stephanie A, Varadhan Ravi, Weiss Carlos O
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, U.S.A.
Stat Med. 2013 Dec 10;32(28):4906-23. doi: 10.1002/sim.5881. Epub 2013 Jun 21.
Understanding how individuals vary in their response to treatment is an important task of clinical research. For standard regression models, a proportional interactions model first described by Follmann and Proschan (1999) offers a powerful approach for identifying effect modification in a randomized clinical trial when multiple variables influence treatment response. In this paper, we present a framework for using the proportional interactions model in the context of a parallel-arm clinical trial with multiple prespecified candidate effect modifiers. To protect against model misspecification, we propose a selection strategy that considers all possible proportional interactions models. We develop a modified Bonferroni correction for multiple testing that accounts for the positive correlation among candidate models. We describe methods for constructing a confidence interval for the proportionality parameter. In simulation studies, we show that our modified Bonferroni adjustment controls familywise error and has greater power to detect proportional interactions compared with multiplcity-corrected subgroup analyses. We demonstrate our methodology by using the Studies of Left Ventricular Dysfunction Treatment trial, a placebo-controlled randomized clinical trial of the efficacy of enalapril to reduce the risk of death or hospitalization in chronic heart failure patients. An R package called anoint is available for implementing the proportional interactions methodology.
了解个体对治疗的反应如何存在差异是临床研究的一项重要任务。对于标准回归模型，Follmann和Proschan（1999年）首次描述的比例交互模型为在多个变量影响治疗反应的随机临床试验中识别效应修正提供了一种强大的方法。在本文中，我们提出了一个在具有多个预先指定的候选效应修正因素的平行组临床试验背景下使用比例交互模型的框架。为防止模型误设，我们提出了一种考虑所有可能的比例交互模型的选择策略。我们针对多重检验开发了一种修正的Bonferroni校正方法，该方法考虑了候选模型之间的正相关性。我们描述了构建比例参数置信区间的方法。在模拟研究中，我们表明，与经多重性校正的亚组分析相比，我们的修正Bonferroni调整控制了族系误差，并且具有更强的检测比例交互作用的能力。我们通过使用左心室功能障碍治疗研究试验（一项关于依那普利降低慢性心力衰竭患者死亡或住院风险疗效的安慰剂对照随机临床试验）来展示我们的方法。一个名为anoint的R包可用于实施比例交互方法。
