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Donglin Zheng, University of North Carolina at Chapel Hill
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| Title: Robust Hybrid Learning For Estimating Personalized Treatment Regimes |
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Abstract: Dynamic treatment regimes (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 dis- eases and disorders calls 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 and hybrid learning method, namely Augmented Multistage Outcome-Weighted Learning (AMOL), to identify optimal DTRs from the Sequential Multiple Assignment Randomization Trials (SMARTs). For multiple-stage SMART studies, we develop a sequentially backward learning method to infer DTRs, making use of the robustness of single-stage outcome weighted learning and the imputation ability of regression model-based Q- learning at each stage. The proposed AMOL remains valid even if the imputation model assumed in the Q-learning is misspecified. We establish theoretical properties of AMOL, including double robustness and efficiency of the imputation step, as well as consistency of estimated rules and 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).
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