title: Comment.
abstract: Xu, Müller, 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.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Jingxiang
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Statistics and Operations Research, Department of Biostatistics, Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, NC, USA.
authors.forename: Rui
authors.initials: R
authors.lastname: Song
authors.affiliation: Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
authors.forename: Yingqi
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: Department of Biostatistics, Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Kosorok
keywords: Dynamic treatment regimes; Multi-stage chemotherapy regimes; O-learning; Q-learning