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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Bibliographic Details
Published in:	Journal of the American Statistical Association Vol. 113; no. 523; pp. 1255 - 1267
Main Authors:	Murray, Thomas A., Yuan, Ying, Thall, Peter F.
Format:	Journal Article
Language:	English
Published:	United States Taylor & Francis 03.07.2018 Taylor & Francis Group,LLC Taylor & Francis Ltd
Subjects:	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
Online Access:	Get full text
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