A Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes

Medical therapy often consists of multiple stages, with a treatment chosen by the physician at each stage based on the patient's history of treatments and clinical outcomes. These decisions can be formalized as a dynamic treatment regime. This article describes a new approach for optimizing dyn...

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Vydáno v:	Journal of the American Statistical Association Ročník 113; číslo 523; s. 1255 - 1267
Hlavní autoři:	Murray, Thomas A., Yuan, Ying, Thall, Peter F.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Taylor & Francis 03.07.2018 Taylor & Francis Group,LLC Taylor & Francis Ltd
Témata:	Algorithms Approximate dynamic programming artificial intelligence Backward induction Bayesian additive regression trees Bayesian analysis Bayesian theory Clinical outcomes Cognitive style Counterfactual thinking Decision analysis Decisions equations Gibbs sampling Health services Inference Machine learning Medical decision making Medical treatment Optimization patients Physicians Potential outcomes Regression analysis Regression models Sampling Simulation Statistical inference Statistical methods Statistics Theory and Methods therapeutics Treatment methods Treatment outcomes Variables Bayesian Additive Regression Trees Gibbs Sampling Potential Outcomes Backward Induction Approximate Dynamic Programming
ISSN:	0162-1459, 1537-274X, 1537-274X
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Shrnutí:	Medical therapy often consists of multiple stages, with a treatment chosen by the physician at each stage based on the patient's history of treatments and clinical outcomes. These decisions can be formalized as a dynamic treatment regime. This article describes a new approach for optimizing dynamic treatment regimes, which bridges the gap between Bayesian inference and existing approaches, like Q-learning. The proposed approach fits a series of Bayesian regression models, one for each stage, in reverse sequential order. Each model uses as a response variable the remaining payoff assuming optimal actions are taken at subsequent stages, and as covariates the current history and relevant actions at that stage. The key difficulty is that the optimal decision rules at subsequent stages are unknown, and even if these decision rules were known the relevant response variables may be counterfactual. However, posterior distributions can be derived from the previously fitted regression models for the optimal decision rules and the counterfactual response variables under a particular set of rules. The proposed approach averages over these posterior distributions when fitting each regression model. An efficient sampling algorithm for estimation is presented, along with simulation studies that compare the proposed approach with Q-learning. Supplementary materials for this article are available online.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Yuan’s and Murray’s research was partially supported by Award Number R01-CA154591 from the National Cancer Institute. The work of the first three authors was partially funded by NIH/NCI grant 5-R01-CA083932.
ISSN:	0162-1459 1537-274X 1537-274X
DOI:	10.1080/01621459.2017.1340887