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Activity Number:
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315
- Biometrics Section Byar Award Student Paper Session I
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Type:
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Topic-Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
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Sponsor:
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Biometrics Section
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Abstract #317334
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Title:
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Nonparametric Dynamic Treatment Regimes for Survival Outcomes
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Author(s):
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Hunyong Cho* and Shannon T. Holloway and Michael Kosorok
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Companies:
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University of North Carolina at Chapel Hill and North Carolina State University and University of North Carolina at Chapel Hill
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Keywords:
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Precision medicine;
Dynamic treatment regime;
Survival analysis;
Reinforcement learning;
Random forest
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Abstract:
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We propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the treatment decision times to be dependent on the failure time and conditionally independent of censoring, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time point. The estimator is constructed using generalized random survival forests, and its consistency is shown using empirical process theory. Simulations and leukemia data analysis results suggest that the new estimator brings higher expected outcomes than existing methods in various settings. An R package dtrSurv is available on CRAN.
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Authors who are presenting talks have a * after their name.
