Information Borrowing from Partially Compatible Trajectories for Estimation of Dynamic Treatment Regimes

TL;DR

This paper introduces new methods for estimating dynamic treatment regimes that relax strict trajectory matching, improving efficiency and stability over traditional inverse probability weighting, with theoretical validation and real-world HIV data application.

Contribution

The paper proposes two novel, computationally feasible methods that relax trajectory compatibility constraints, enhancing efficiency and stability in DTR estimation.

Findings

Both estimators are consistent and more efficient than standard IPW.

Simulation studies show improved finite-sample stability.

Application to HIV data demonstrates practical utility.

Abstract

Dynamic Treatment Regimes (DTRs) provide a systematic framework for optimizing sequential decision-making in chronic disease management, where therapies must adapt to patients' evolving clinical profiles. Inverse probability weighting (IPW) is a cornerstone methodology for estimating regime values from observational data due to its intuitive formulation and established theoretical properties, yet standard IPW estimators face significant limitations, including variance instability and data inefficiency. A fundamental but underexplored source of inefficiency lies in the strict alignment requirement between observed and target treatment trajectories, which fails to account for partial compatibility and discards substantial information from individuals with only minimal deviations from the regime. We propose two novel methodologies that relax the strict inclusion rule through flexible compatibility mechanisms. Both methods provide computationally tractable alternatives that can be easily integrated into existing IPW workflows, offering more efficient approaches to DTR estimation. Theoretical analysis demonstrates that both estimators preserve consistency while achieving superior finite-sample efficiency compared to standard IPW, and comprehensive simulation studies confirm improved stability. We illustrate the practical utility of our methods through an application to HIV treatment data from the AIDS Clinical Trials Group Study 175 (ACTG175).