Title | Greedy outcome weighted tree learning of optimal personalized treatment rules. |
Publication Type | Journal Article |
Year of Publication | 2017 |
Authors | Zhu, Ruoqing, Ying-Qi Zhao, Guanhua Chen, Shuangge Ma, and Hongyu Zhao |
Journal | Biometrics |
Volume | 73 |
Issue | 2 |
Pagination | 391-400 |
Date Published | 2017 Jun |
ISSN | 1541-0420 |
Keywords | Algorithms, Humans, Periodontics |
Abstract | 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 high-dimensional 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. |
DOI | 10.1111/biom.12593 |
Alternate Journal | Biometrics |
Original Publication | Greedy outcome weighted tree learning of optimal personalized treatment rules. |
PubMed ID | 27704531 |
PubMed Central ID | PMC5378692 |
Grant List | R01 DK108073 / DK / NIDDK NIH HHS / United States UL1 TR001863 / TR / NCATS NIH HHS / United States R21 CA191383 / CA / NCI NIH HHS / United States P01 CA154295 / CA / NCI NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States P30 CA016359 / CA / NCI NIH HHS / United States U10 CA180819 / CA / NCI NIH HHS / United States R01 GM059507 / GM / NIGMS NIH HHS / United States P50 CA196530 / CA / NCI NIH HHS / United States |
Greedy outcome weighted tree learning of optimal personalized treatment rules.
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