Dynamic treatment regime characterization via value function surrogate with an application to partial compliance

TL;DR

This paper introduces a Gaussian process surrogate method to characterize classes of dynamic treatment regimes in precision medicine, specifically addressing partial compliance in wound management for peripheral artery disease.

Contribution

It presents a novel approach to characterize dynamic treatment regimes beyond optimality, facilitating clinical translation in the context of partial compliance.

Findings

Effective characterization of treatment regimes in partial compliance scenarios

Enhanced translation of statistical regimes into clinical practice

Demonstrated approach with real-world wound management data

Abstract

Precision medicine is a promising framework for generating evidence to improve health and health care. Yet, a gap persists between the ever-growing number of statistical precision medicine strategies for evidence generation and implementation in real world clinical settings, and the strategies for closing this gap will likely be context dependent. In this paper, we consider the specific context of partial compliance to wound management among patients with peripheral artery disease. Through the use of a Gaussian process surrogate for the value function, we expand beyond the common precision medicine task of learning an optimal dynamic treatment regime to characterization of classes of dynamic treatment regimes and how those findings can be translated into clinical contexts.