Petkova Eva, Tarpey Thaddeus, Su Zhe, Ogden R Todd
Department of Child and Adolescent Psychiatry, New York University, 1 Park Ave., New York, NY 10016, USA and Nathan Kline Institute for Psychiatric Research, 140 Old Orangeburg Road, Orangeburg, NY 10962, USA
Department of Mathematics and Statistics, Wright State University, 3640 Colonel Glenn Hwy, Dayton, OH 45435, USA and Department of Child and Adolescent Psychiatry, New York University, 1 Park Ave., New York, NY 10016, USA.
Biostatistics. 2017 Jan;18(1):105-118. doi: 10.1093/biostatistics/kxw035. Epub 2016 Jul 27.
In a randomized clinical trial (RCT), it is often of interest not only to estimate the effect of various treatments on the outcome, but also to determine whether any patient characteristic has a different relationship with the outcome, depending on treatment. In regression models for the outcome, if there is a non-zero interaction between treatment and a predictor, that predictor is called an "effect modifier". Identification of such effect modifiers is crucial as we move towards precision medicine, that is, optimizing individual treatment assignment based on patient measurements assessed when presenting for treatment. In most settings, there will be several baseline predictor variables that could potentially modify the treatment effects. This article proposes optimal methods of constructing a composite variable (defined as a linear combination of pre-treatment patient characteristics) in order to generate an effect modifier in an RCT setting. Several criteria are considered for generating effect modifiers and their performance is studied via simulations. An example from a RCT is provided for illustration.
在一项随机临床试验（RCT）中，人们通常不仅感兴趣于估计各种治疗对结局的影响，还想确定是否有任何患者特征根据治疗情况与结局存在不同的关系。在结局的回归模型中，如果治疗与一个预测变量之间存在非零交互作用，那么该预测变量就被称为“效应修饰因素”。随着我们迈向精准医学，即根据治疗时评估的患者测量值优化个体治疗分配，识别此类效应修饰因素至关重要。在大多数情况下，会有几个基线预测变量可能潜在地修饰治疗效果。本文提出了构建复合变量（定义为治疗前患者特征的线性组合）的最优方法，以便在RCT环境中生成一个效应修饰因素。考虑了几种生成效应修饰因素的标准，并通过模拟研究了它们的性能。提供了一个来自RCT的例子用于说明。
