Dynamic treatment regimes (DTRs) consist of a sequence of decision rules, one per stage of intervention, that finds effective treatments for individual patients according to patient information history. DTRs can be estimated from models which include the interaction between treatment and a small number of covariates which are often chosen a priori. However, with increasingly large and complex data being collected, it is difficult to know which prognostic factors might be relevant in the treatment rule. Therefore, a more data-driven approach of selecting these covariates might improve the estimated decision rules and simplify models to make them easier to interpret. We propose a variable selection method for DTR estimation using penalized dynamic weighted least squares. Our method has the strong heredity property, that is, an interaction term can be included in the model only if the corresponding main terms have also been selected. Through simulations, we show our method has both the double robustness property and the oracle property, and the newly proposed methods compare favorably with other variable selection approaches.
翻译:动态治疗制度(DTRs)由一系列决定规则组成,每个干预阶段一个,根据病人信息史发现对个别病人的有效治疗。DTRs可以从模型中估算,模型包括治疗与经常按先验选择的少量共变体之间的相互作用。然而,随着收集的数据日益庞大和复杂,很难知道哪些预测因素可能与治疗规则相关。因此,选择这些共变体的更注重数据的方法可能会改进估计的决定规则并简化模型,使其更容易解释。我们建议了一种变量选择方法,用于使用受罚的动态加权最小方块进行DTR估计。我们的方法具有很强的遗传属性,也就是说,只有在也选择了相应的主要术语的情况下,才能在模型中包括互动术语。我们通过模拟表明我们的方法既具有双重稳健的属性,又具有甲骨骼属性,而且新提出的方法与其他变量选择方法比较得当。