Doubly-Robust Dynamic Treatment Regimen Estimation for Binary Outcomes

TL;DR

This paper introduces a new doubly-robust method for estimating optimal dynamic treatment regimes with binary outcomes, addressing a gap in personalized medicine where previous methods focused on continuous outcomes.

Contribution

It proposes a novel balancing weight criterion and binary pseudo-outcomes to improve DTR estimation with binary data, supported by theoretical analysis and simulations.

Findings

Method is doubly-robust and reliable in simulations

Successfully applied to e-cigarette and smoking cessation data

Outperforms existing approaches in binary outcome settings

Abstract

In precision medicine, Dynamic Treatment Regimes (DTRs) are treatment protocols that adapt over time in response to a patient's observed characteristics. A DTR is a set of decision functions that takes an individual patient's information as arguments and outputs an action to be taken. Building on observed data, the aim is to identify the DTR that optimizes expected patient outcomes. Multiple methods have been proposed for optimal DTR estimation with continuous outcomes. However, optimal DTR estimation with binary outcomes is more complicated and has received comparatively little attention. Solving a system of weighted generalized estimating equations, we propose a new balancing weight criterion to overcome the misspecification of generalized linear models' nuisance components. We construct binary pseudo-outcomes, and develop a doubly-robust and easy-to-use method to estimate an optimal DTR with binary outcomes. We also outline the underlying theory, which relies on the balancing property of the weights; provide simulation studies that verify the double-robustness of our method; and illustrate the method in studying the effects of e-cigarette usage on smoking cessation, using observational data from the Population Assessment of Tobacco and Health (PATH) study.