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Comment

Author

Listed:
  • Jingxiang Chen
  • Yufeng Liu
  • Donglin Zeng
  • Rui Song
  • Yingqi Zhao
  • Michael R. Kosorok

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.

Suggested Citation

  • Jingxiang Chen & Yufeng Liu & Donglin Zeng & Rui Song & Yingqi Zhao & Michael R. Kosorok, 2016. "Comment," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 942-947, July.
    • Handle: RePEc:taf:jnlasa:v:111:y:2016:i:515:p:942-947
    DOI: 10.1080/01621459.2016.1200914
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    References listed on IDEAS

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    1. Ailin Fan & Rui Song & Wenbin Lu, 2017. "Change-Plane Analysis for Subgroup Detection and Sample Size Calculation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 769-778, April.
    2. S. A. Murphy, 2003. "Optimal dynamic treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 331-355, May.
    3. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2013. "Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions," Biometrika, Biometrika Trust, vol. 100(3), pages 681-694.
    4. Ying-Qi Zhao & Donglin Zeng & Eric B. Laber & Michael R. Kosorok, 2015. "New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 583-598, June.
    5. Juan Shen & Xuming He, 2015. "Inference for Subgroup Analysis With a Structured Logistic-Normal Mixture Model," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 303-312, March.
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