Greedy outcome weighted tree learning of optimal personalized treatment rules

Ruoqing Zhu, Ying Qi Zhao, Guanhua Chen, Shuangge Ma, Hongyu Zhao

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)391-400
Number of pages10
JournalBiometrics
Volume73
Issue number2
DOIs
StatePublished - Jun 2017

Keywords

  • High-dimensional data
  • Optimal treatment rules
  • Personalized medicine
  • Reinforcement learning trees
  • Survival analysis
  • Tree-based method

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Greedy outcome weighted tree learning of optimal personalized treatment rules'. Together they form a unique fingerprint.

Cite this