Guidance on Individualized Treatment Rule Estimation in High Dimensions

TL;DR

This paper evaluates various methods for estimating individualized treatment rules in high-dimensional settings, highlighting their performance, proposing a covariate filtering technique, and providing practical guidance for clinical applications.

Contribution

It offers a comprehensive comparison of state-of-the-art estimators in high-dimensional contexts and introduces a novel covariate filtering procedure to enhance accuracy and interpretability.

Findings

Filtering improves estimator accuracy

High-dimensional methods vary in performance

Guidelines for practitioners are provided

Abstract

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 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. 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.