Estimating Individualized Treatment Rules for Ordinal Treatments

TL;DR

This paper introduces a novel method for estimating optimal individualized treatment rules in ordinal treatment settings, extending outcome weighted learning techniques with theoretical guarantees and demonstrating superior performance in simulations and real datasets.

Contribution

It proposes a new data duplication technique with a piecewise convex loss function for ordinal treatments, establishing Fisher consistency and convergence properties.

Findings

The method outperforms existing alternatives in simulations.

It achieves high accuracy in real-world datasets.

Theoretical properties ensure reliable estimation.

Abstract

Precision medicine is an emerging scientific topic for disease treatment and prevention that takes into account individual patient characteristics. It is an important direction for clinical research, and many statistical methods have been recently proposed. One of the primary goals of precision medicine is to obtain an optimal individual treatment rule (ITR), which can help make decisions on treatment selection according to each patient's specific characteristics. Recently, outcome weighted learning (OWL) has been proposed to estimate such an optimal ITR in a binary treatment setting by maximizing the expected clinical outcome. However, for ordinal treatment settings, such as individualized dose finding, it is unclear how to use OWL. In this paper, we propose a new technique for estimating ITR with ordinal treatments. In particular, we propose a data duplication technique with a piecewise convex loss function. We establish Fisher consistency for the resulting estimated ITR under certain conditions, and obtain the convergence and risk bound properties. Simulated examples and two applications to datasets from an irritable bowel problem and a type 2 diabetes mellitus observational study demonstrate the highly competitive performance of the proposed method compared to existing alternatives.