Estimation of Individualized Decision Rules Based on an Optimized Covariate-Dependent Equivalent of Random Outcomes

TL;DR

This paper introduces a novel covariate-dependent equivalent framework for estimating individualized decision rules, improving precision medicine outcomes especially under heavy-tail data distributions.

Contribution

It extends the optimal IDR framework by incorporating an optimized covariate-dependent equivalent based on OCE, enhancing decision-making in precision medicine.

Findings

Outperforms existing methods in heavy-tail distribution scenarios

Broadens the OCE concept for individualized decision making

Demonstrates superior estimation of optimal IDRs

Abstract

Recent exploration of optimal individualized decision rules (IDRs) for patients in precision medicine has attracted a lot of attention due to the heterogeneous responses of patients to different treatments. In the existing literature of precision medicine, an optimal IDR is defined as a decision function mapping from the patients' covariate space into the treatment space that maximizes the expected outcome of each individual. Motivated by the concept of Optimized Certainty Equivalent (OCE) introduced originally in \cite{ben1986expected} that includes the popular conditional-value-of risk (CVaR) \cite{rockafellar2000optimization}, we propose a decision-rule based optimized covariates dependent equivalent (CDE) for individualized decision making problems. Our proposed IDR-CDE broadens the existing expected-mean outcome framework in precision medicine and enriches the previous concept of the OCE. Numerical experiments demonstrate that our overall approach outperforms existing methods in estimating optimal IDRs under heavy-tail distributions of the data.