Adaptive Weight Learning for Multiple Outcome Optimization With Continuous Treatment

TL;DR

This paper introduces a novel data-driven algorithm for developing individualized treatment regimes that optimize multiple outcomes with continuous treatment options, considering patient preferences and clinician expertise, especially in precision medicine contexts.

Contribution

It presents a new method for estimating outcome weights and treatment decisions in scenarios with continuous treatments and multiple outcomes, extending beyond traditional single-outcome, categorical approaches.

Findings

The method effectively estimates outcome weights and treatment regimes in simulations.

Application to radiation oncology demonstrates practical utility.

The approach facilitates variable selection and inference for treatment decision variables.

Abstract

To promote precision medicine, individualized treatment regimes (ITRs) are crucial for optimizing the expected clinical outcome based on patient-specific characteristics. However, existing ITR research has primarily focused on scenarios with categorical treatment options and a single outcome. In reality, clinicians often encounter scenarios with continuous treatment options and multiple, potentially competing outcomes, such as medicine efficacy and unavoidable toxicity. To balance these outcomes, a proper weight is necessary, which should be learned in a data-driven manner that considers both patient preference and clinician expertise. In this paper, we present a novel algorithm for developing individualized treatment regimes (ITRs) that incorporate continuous treatment options and multiple outcomes, utilizing observational data. Our approach assumes that clinicians are optimizing individualized patient utilities with sub-optimal treatment decisions that are at least better than random assignment. Treatment assignment is assumed to directly depend on the true underlying utility of the treatment rather than patient characteristics. The proposed method simultaneously estimates the weighting of composite outcomes and the decision-making process, allowing for construction of individualized treatment regimes with continuous doses. The proposed estimators can be used for inference and variable selection, facilitating the identification of informative treatment assignments and preference-associated variables. We evaluate the finite sample performance of our proposed method via simulation studies and apply it to a real data application of radiation oncology analysis.