Robust Hybrid Learning for Estimating Personalized Dynamic Treatment Regimens

TL;DR

This paper introduces a robust hybrid method called AMOL that combines outcome-weighted learning and Q-learning to improve the estimation of personalized dynamic treatment regimens from SMART trial data, accommodating complex, heterogeneous, and multi-stage treatment scenarios.

Contribution

The paper proposes a novel AMOL method that generalizes outcome-weighted learning, reduces variability, and incorporates doubly robust augmentation for better DTR estimation, even with model misspecification.

Findings

AMOL outperforms existing methods in simulations.

AMOL provides more stable and efficient treatment rules.

Application to real SMART data demonstrates practical utility.

Abstract

Dynamic treatment regimens (DTRs) are sequential decision rules tailored at each stage by potentially time-varying patient features and intermediate outcomes observed in previous stages. The complexity, patient heterogeneity and chronicity of many diseases and disorders call for learning optimal DTRs which best dynamically tailor treatment to each individual's response over time. Proliferation of personalized data (e.g., genetic and imaging data) provides opportunities for deep tailoring as well as new challenges for statistical methodology. In this work, we propose a robust hybrid approach referred as Augmented Multistage Outcome-Weighted Learning (AMOL) to integrate outcome-weighted learning and Q-learning to identify optimal DTRs from the Sequential Multiple Assignment Randomization Trials (SMARTs). We generalize outcome weighted learning (O-learning; Zhao et al.~2012) to allow for negative outcomes; we propose methods to reduce variability of weights in O-learning to achieve numeric stability and higher efficiency; finally, for multiple-stage SMART studies, we introduce doubly robust augmentation to machine learning based O-learning to improve efficiency by drawing information from regression model-based Q-learning at each stage. The proposed AMOL remains valid even if the Q-learning model is misspecified. We establish the theoretical properties of AMOL, including the consistency of the estimated rules and the rates of convergence to the optimal value function. The comparative advantage of AMOL over existing methods is demonstrated in extensive simulation studies and applications to two SMART data sets: a two-stage trial for attention deficit and hyperactive disorder (ADHD) and the STAR*D trial for major depressive disorder (MDD).