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. 2016;111(515):942-947.
doi: 10.1080/01621459.2016.1200914. Epub 2016 Oct 18.

Comment

Jingxiang Chen  1 , Yufeng Liu  2 , Donglin Zeng  1 , Rui Song  3 , Yingqi Zhao  4 , Michael R Kosorok  5
Affiliations

Comment

Jingxiang Chen et al. J Am Stat Assoc. 2016.

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.

Keywords: Dynamic treatment regimes; Multi-stage chemotherapy regimes; O-learning; Q-learning.

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Figures

Figure 1
Figure 1
Redefinition of the Scheme under the proposed Q-learning Framework. The states in red square boxes (i.e., initialization, resistance and progression) are the treatment decision-making points that are used to split the two stages. Complete remission (C) is not considered as a splitting point since no decision action can be taken. Censoring time could happen at the end of each stage.
See this image and copyright information in PMC

References

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