A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates

We consider a setting in which we have a treatment and a potentially large number of covariates for a set of observations, and wish to model their relationship with an outcome of interest. We propose a simple method for modeling interactions between the treatment and covariates. The idea is to modify the covariate in a simple way, and then fit a standard model using the modified covariates and no main effects. We show that coupled with an efficiency augmentation procedure, this method produces clinically meaningful estimators in a variety of settings. It can be useful for practicing personalized medicine: determining from a large set of biomarkers, the subset of patients that can potentially benefit from a treatment. We apply the method to both simulated datasets and real trial data. The modified covariates idea can be used for other purposes, for example, large scale hypothesis testing for determining which of a set of covariates interact with a treatment variable. Supplementary materials for this article are available online.

我们考虑这样一种研究场景：针对一组观测样本，存在一项治疗干预 (treatment) 以及数量可观的协变量 (covariate)，我们希望构建它们与目标结局之间的关联模型。我们提出了一种简便的方法，用于建模治疗干预与协变量之间的交互效应。该方法的核心思路是对协变量进行简单的变换处理，随后使用变换后的协变量拟合标准模型，且不纳入主效应项。我们证明，将该方法与效率增强流程相结合后，可在多种场景下生成具有临床意义的估计量。该方法可应用于个性化医疗实践：从海量生物标志物 (biomarkers) 中筛选出有望从某一治疗干预中获益的患者亚群。我们将该方法应用于模拟数据集与真实临床试验数据中。这种协变量变换的思路还可拓展至其他应用场景，例如用于大规模假设检验，以识别与某治疗变量存在交互效应的协变量子集。本文的补充材料可在线获取。