Flexible Inference of Optimal Individualized Treatment Strategy in Covariate Adjusted Randomization with Multiple Covariates

TL;DR

This paper introduces a flexible statistical method for estimating individualized treatment effects in clinical trials with many covariates, enabling better personalized treatment decisions without the curse of dimensionality.

Contribution

It proposes a novel class of estimators that accurately estimate treatment effect functions and indices while avoiding baseline response estimation, improving personalized treatment inference.

Findings

Effective in simulations and real clinical data

Identifies predictive covariates and treatment regions

Handles high-dimensional covariates efficiently

Abstract

To maximize clinical benefit, clinicians routinely tailor treatment to the individual characteristics of each patient, where individualized treatment rules are needed and are of significant research interest to statisticians. In the covariate-adjusted randomization clinical trial with many covariates, we model the treatment effect with an unspecified function of a single index of the covariates and leave the baseline response completely arbitrary. We devise a class of estimators to consistently estimate the treatment effect function and its associated index while bypassing the estimation of the baseline response, which is subject to the curse of dimensionality. We further develop inference tools to identify predictive covariates and isolate effective treatment region. The usefulness of the methods is demonstrated in both simulations and a clinical data example.