Bayesian Nonparametric Estimation for Dynamic Treatment Regimes With Sequential Transition Times Comment

• 成果类型: Editorial Material
• 署名作者: Chen, Jingxiang; Liu, Yufeng; Zeng, Donglin; Song, Rui; Zhao, Yingqi; Kosorok, Michael R.
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine; North Carolina State University; Fred Hutchinson Cancer Center; University of North Carolina; University of North Carolina Chapel Hill; University of North Carolina School of Medicine
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2016.1200914
• 发表日期: 2016
• 页码: 942-947
• 关键词: censored-data; trials

摘要

Xu, Muller, Wahed, and Thall proposed a Bayesian model to analyze an acute leukemia study involving multi :stage chemotherapy regimes. We discuss two alternative methods, Q-learning and O-learning, to solve the same problem from the machine learning point of view. The numerical studies show that these methods can be flexible and have advantages in some situations to handle treatment heterogeneity while being robust to model misspecification.