Risk-Aware Quantile Learning for Personalized Dynamic Treatment Regimes

• URL: http://arxiv.org/abs/2608.05434v1
• Date: Wed, 05 Aug 2026 22:04:47 GMT
• Title: Risk-Aware Quantile Learning for Personalized Dynamic Treatment Regimes
• Abstract summary: Clinicians may need to simultaneously optimize clinically relevant tails of the outcome distribution, control treatment-related risk, and choose among multiple treatment options.<n>Existing quantile dynamic treatment regime (DTR) methods capture distributional features of treatment outcomes but remain largely restricted to efficacy-only objectives and binary treatments.<n>We propose Risk-Aware Quantile Dynamic Treatment Regimes (RQDTR), a unified framework that optimize a prespecified quantile of the cumulative potential outcome while explicitly incorporating treatment-related risk.
• Score: 1.2074766935042585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sequential clinical decision-making often involves more than maximizing average efficacy. Clinicians may need to simultaneously optimize clinically relevant tails of the outcome distribution, control treatment-related risk, and choose among multiple treatment options. Existing quantile dynamic treatment regime (DTR) methods capture distributional features of treatment outcomes but remain largely restricted to efficacy-only objectives and binary treatments. To address these limitations, we propose Risk-Aware Quantile Dynamic Treatment Regimes (RQDTR), a unified framework that optimizes a prespecified quantile of the cumulative potential outcome while explicitly incorporating treatment-related risk. We also develop an angle-based formulation for jointly learning decision rules across multiple treatment categories. Our framework includes three interpretable subclasses: efficacy-only quantile learning, constraint-based learning with population-level risk control, and utility-based learning through a composite benefit-risk utility. Theoretically, we establish identification and oracle equivalence, Fisher consistency of the smoothed surrogate, consistency of the estimated regime, and finite-sample performance error rates. Extensive simulation studies and applications to All of Us major depressive disorder and MIMIC-III sepsis data demonstrate that RQDTR improves tail-oriented efficacy and achieves more favorable benefit-risk trade-offs than existing quantile DTR methods.

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