A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates
复制标题
DOI：
10.1080/01621459.2014.951443
复制
发表时间：
2014-12-01
影响因子：
3.7
通讯作者：
Tibshirani, Robert
中科院分区：
文献类型：
--
作者：
Tian, Lu;Alizadeh, Ash A.;Tibshirani, Robert
我们考虑这样一个环境，在这个环境中，我们对一组观察结果进行了处理，并可能有大量的协变量，并希望用感兴趣的结果来模拟它们的关系。我们提出了一种简单的方法来模拟治疗和协变量之间的交互作用。其思想是以一种简单的方式修改协变量，然后使用修改后的协变量来拟合标准模型，并且没有主效应。我们表明，结合效率增强程序，该方法在各种设置下产生了具有临床意义的估计值。这对于实践个性化医学可能很有用：从一大组生物标记物中确定可能从治疗中受益的患者子集。我们将该方法应用于模拟数据集和实际试验数据。改进的协变量思想可以用于其他目的，例如，用于确定一组协变量中的哪些与治疗变量相互作用的大规模假设检验。这篇文章的补充材料可以在网上找到。
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.