Guidance on individualized treatment rule estimation in high dimensions

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' rule quality, interpretability, and computational efficiency. 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. Owing to individualized treatment rule estimators' poor interpretability, we propose a novel pre-treatment covariate filtering procedure based on recent work for uncovering treatment effect modifiers. We show that it improves estimators' rule quality and interpretability. All code is made publicly available, facilitating modifications and extensions to our simulation study.