Multicategory Matched Learning for Estimating Optimal Individualized Treatment Rules in Observational Studies with Application to a Hepatocellular Carcinoma Study

TL;DR

This paper introduces a novel matching-based machine learning approach for estimating optimal individualized treatment rules in observational studies with multiple treatments, applicable to fully observed or censored outcomes, supported by theoretical analysis and empirical validation.

Contribution

It develops a new matching-based method for multi-treatment ITR estimation in observational data, extending existing binary-treatment approaches with theoretical guarantees.

Findings

The proposed method performs well in simulations.

It effectively handles censored outcomes.

Application to a hepatocellular carcinoma study demonstrates practical utility.

Abstract

One primary goal of precision medicine is to estimate the individualized treatment rules (ITRs) that optimize patients' health outcomes based on individual characteristics. Health studies with multiple treatments are commonly seen in practice. However, most existing ITR estimation methods were developed for the studies with binary treatments. Many require that the outcomes are fully observed. In this paper, we propose a matching-based machine learning method to estimate the optimal ITRs in observational studies with multiple treatments when the outcomes are fully observed or right-censored. We establish theoretical property for the proposed method. It is compared with the existing competitive methods in simulation studies and a hepatocellular carcinoma study.