Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This article develops a sparse additive model focused on estimation of treatment effect modification with simultaneous treatment effect-modifier selection. We propose a version of the sparse additive model uniquely constrained to estimate the interaction effects between treatment and pretreatment covariates, while leaving the main effects of the pretreatment covariates unspecified. The proposed regression model can effectively identify treatment effect-modifiers that exhibit possibly nonlinear interactions with the treatment variable that are relevant for making optimal treatment decisions. A set of simulation experiments and an application to a dataset from a randomized clinical trial are presented to demonstrate the method.

中文翻译：

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用于治疗效果调节剂选择的稀疏加性模型。


稀疏加性建模是一类执行高维非参数回归的有效方法。本文开发了一种稀疏加性模型，重点关注同时治疗效果调节剂选择的治疗效果调节的估计。我们提出了稀疏加性模型的一个版本，该模型独特地限制了估计治疗和预处理协变量之间的交互作用，同时未指定预处理协变量的主要影响。所提出的回归模型可以有效地识别与制定最佳治疗决策相关的治疗变量可能表现出非线性相互作用的治疗效果调节剂。提出了一组模拟实验以及对随机临床试验数据集的应用来演示该方法。