Individualized treatment rules, cornerstones of precision medicine, inform patient treatment decisions with the goal of optimizing patient outcomes. These rules are generally unknown functions of patients' pre-treatment covariates, meaning they must be estimated from clinical or observational study data. Myriad methods have been developed to learn these rules, and these procedures are demonstrably successful in traditional asymptotic settings with moderate number of covariates. The finite-sample performance of these methods in high-dimensional covariate settings, which are increasingly the norm in modern clinical trials, has not been well characterized, however. We perform a comprehensive comparison of state-of-the-art individualized treatment rule estimators, assessing performance on the basis of the estimators' accuracy, interpretability, and computational efficacy. Sixteen data-generating processes with continuous outcomes and binary treatment assignments are considered, reflecting a diversity of randomized and observational studies. We summarize our findings and provide succinct advice to practitioners needing to estimate individualized treatment rules in high dimensions. All code is made publicly available, facilitating modifications and extensions to our simulation study. A novel pre-treatment covariate filtering procedure is also proposed and is shown to improve estimators' accuracy and interpretability.

翻译：个性化治疗规则作为精准医学的基石，旨在通过指导患者治疗决策以优化临床结局。这些规则通常是患者治疗前协变量的未知函数，意味着必须从临床或观察性研究数据中进行估计。目前已发展出多种学习此类规则的方法，这些方法在协变量数量适中的传统渐近设定中已证明是成功的。然而，这些方法在高维协变量设定（现代临床试验日益普遍的范式）中的有限样本性能尚未得到充分表征。我们对前沿的个性化治疗规则估计方法进行了全面比较，从估计准确性、可解释性和计算效率三个维度评估其性能。研究考虑了十六种具有连续结局和二元治疗分配的数据生成过程，涵盖了随机化与观察性研究的多种情境。我们总结了研究发现，并为需要在高维背景下估计个性化治疗规则的研究者提供了简明建议。所有代码均已公开，便于对本模拟研究进行修改与扩展。本文还提出了一种新颖的治疗前协变量筛选方法，该方法被证明能有效提升估计器的准确性与可解释性。