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Statistica Sinica 29 (2019), 1633-1710

ENTROPY LEARNING FOR
DYNAMIC TREATMENT REGIMES
Binyan Jiang, Rui Song, Jialiang Li and Donglin Zeng
The Hong Kong Polytechnic University, North Carolina State University
National University of Singapore and University of North Carolina, Chapel Hill

Abstract: Estimating optimal individualized treatment rules (ITRs) in single- or multi-stage clinical trials is a key element of personalized medicine and, as a result, is receiving increasing attention within the statistical community. Recent works have suggested that machine learning approaches can provide significantly better estimations than those of model-based methods. However, a proper inference for estimated ITRs has not been well established for machine learning-based approaches. In this paper, we propose an entropy learning approach for estimating optimal ITRs. We obtain the asymptotic distributions for the estimated rules in order to provide a valid inference. The proposed approach is demonstrated to perform well through extensive simulation studies. Finally, we analyze data from a multi-stage clinical trial for depression patients. Our results offer novel findings not revealed by existing approaches.

Key words and phrases: Dynamic treatment regime, entropy learning, personalized medicine.

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