Greedy outcome weighted tree learning of optimal personalized treatment rules.
Biometrics
; 73(2): 391-400, 2017 06.
Article
em En
| MEDLINE
| ID: mdl-27704531
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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Periodontia
Limite:
Humans
Idioma:
En
Revista:
Biometrics
Ano de publicação:
2017
Tipo de documento:
Article
País de afiliação:
Estados Unidos