Greedy Outcome Weighted Tree Learning of Optimal Personalized Treatment Rules

We propose a subgroup identification approach for inferring optimal and interpretable personalized treatment rules with high-dimensional covariates. Our approach is based on a two-step greedy tree algorithm to pursue signals in a highdimensional space. In the first step, we transform the treatment s...

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Bibliographic Details
Published in:Biometrics 2017-06, Vol.73 (2), p.391-400
Main Authors: Zhu, Ruoqing, Zhao, Ying-Qi, Chen, Guanhua, Ma, Shuangge, Zhao, Hongyu
Format: Article
Language:English
Subjects:
Algorithms
Aversion learning
BIOMETRIC METHODOLOGY
Breast cancer
Cancer
Classification
Computer simulation
Encyclopedias
Failure
Greedy algorithms
High‐dimensional data
Humans
Machine learning
Optimal treatment rules
Optimization
Periodontics
Personalized medicine
Reinforcement learning trees
Serial learning
Splitting
Survival
Survival analysis
Tamoxifen
Tree‐based method
Weighting
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Summary:We propose a subgroup identification approach for inferring optimal and interpretable personalized treatment rules with high-dimensional covariates. Our approach is based on a two-step greedy tree algorithm to pursue signals in a highdimensional space. In the first step, we transform the treatment selection problem into a weighted classification problem that can utilize tree-based methods. In the second step, we adopt a newly proposed tree-based method, known as reinforcement learning trees, to detect features involved in the optimal treatment rules and to construct binary splitting rules. The method is further extended to right censored survival data by using the accelerated failure time model and introducing double weighting to the classification trees. The performance of the proposed method is demonstrated via simulation studies, as well as analyses of the Cancer Cell Line Encyclopedia (CCLE) data and the Tamoxifen breast cancer data.
ISSN:0006-341X
1541-0420
DOI:10.1111/biom.12593