An individualized treatment rule (ITR) is a decision rule that aims to improve individual patients health outcomes by recommending optimal treatments according to patients specific information. In observational studies, collected data may contain many variables that are irrelevant for making treatment decisions. Including all available variables in the statistical model for the ITR could yield a loss of efficiency and an unnecessarily complicated treatment rule, which is difficult for physicians to interpret or implement. Thus, a data-driven approach to select important tailoring variables with the aim of improving the estimated decision rules is crucial. While there is a growing body of literature on selecting variables in ITRs with continuous outcomes, relatively few methods exist for discrete outcomes, which pose additional computational challenges even in the absence of variable selection. In this paper, we propose a variable selection method for ITRs with discrete outcomes. We show theoretically and empirically that our approach has the double robustness property, and that it compares favorably with other competing approaches. We illustrate the proposed method on data from a study of an adaptive web-based stress management tool to identify which variables are relevant for tailoring treatment.

翻译：个体化治疗规则（ITR）是一种决策规则，旨在根据患者的具体信息推荐最优治疗方案，从而改善个体患者的健康结局。在观察性研究中，收集的数据可能包含许多与治疗决策无关的变量。将所有可用变量纳入ITR的统计模型可能导致效率损失以及不必要的复杂治疗规则，使临床医生难以解释或实施。因此，开发一种基于数据驱动的方法来选择重要的定制变量以改进估计决策规则至关重要。尽管关于连续结果ITR的变量选择研究日益增多，但针对离散结果的方法相对较少，即便不考虑变量选择，离散结果也会带来额外的计算挑战。本文提出了一种适用于离散结果ITR的变量选择方法。我们从理论和实证角度证明，该方法具有双重稳健性，且优于其他竞争方法。我们将所提方法应用于一项自适应网络压力管理工具的研究数据，以识别哪些变量与定制治疗方案相关。