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：个性化治疗规则是精准医学的基石，旨在通过指导患者治疗决策优化预后效果。这些规则通常是患者治疗前协变量的未知函数，需从临床或观察性研究数据中估计。学界已开发出多种方法用于学习此类规则，且在协变量数量适中的传统渐近设定中展现出显著成效。然而，在协变量高维场景（这一特征在现代临床试验中日益成为常态）下，这些方法的有限样本性能尚未得到充分刻画。本研究对前沿个性化治疗规则估计方法进行系统比较，从估计精度、可解释性及计算效能三个维度评估性能。我们考虑了十六种连续结局变量与二元治疗分配的数据生成过程，涵盖随机化研究与观察性研究的多样性场景。基于研究结果总结发现，并为实践者在高维场景下估计个性化治疗规则提供简洁建议。所有代码均已公开，便于对本模拟研究进行修改与扩展。我们还提出了一种新颖的治疗前协变量过滤程序，并证明其能有效提升估计器的精度与可解释性。