论文标题
用于治疗效果模型选择的约束稀疏添加剂模型
A constrained sparse additive model for treatment effect-modifier selection
论文作者
论文摘要
稀疏添加剂建模是进行高维非参数回归的一类有效方法。本文开发了一个稀疏的加性模型，重点是估计治疗效应调整和同时治疗效应模型选择。我们提出了一个稀疏的加性模型的版本，以唯一限制，以估计处理和预处理协变量之间的相互作用效应，同时未指定的预处理协变量的主要影响。提出的回归模型可以有效地识别出可能与治疗变量表现出非线性相互作用的治疗效应模型，这与做出最佳治疗决策有关。提出了一组仿真实验和对数据集的应用程序，以证明该方法。
Sparse additive modeling is a class of effective methods for performing high-dimensional nonparametric regression. This paper 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.
