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＜電子ブック＞
Statistical Methods for Dynamic Treatment Regimes : Reinforcement Learning, Causal Inference, and Personalized Medicine / by Bibhas Chakraborty, Erica E.M. Moodie
(Statistics for Biology and Health. ISSN:21975671 ; 76)

版	1st ed. 2013.
出版者	(New York, NY : Springer New York : Imprint: Springer)
出版年	2013
大きさ	XVI, 204 p : online resource
著者標目	*Chakraborty, Bibhas author Moodie, Erica E.M author SpringerLink (Online service)
件　名	LCSH:Biometry LCSH:Statistics  LCSH:Medical informatics FREE:Biostatistics FREE:Statistics FREE:Health Informatics
一般注記	Introduction -- The Data: Observational Studies and Sequentially Randomized Trials -- Statistical Reinforcement Learning -- Estimation of Optimal DTRs by Modeling Contrasts of Conditional Mean Outcomes -- Estimation of Optimal DTRs by Directly Modeling Regimes -- G-computation: Parametric Estimation of Optimal DTRs -- Estimation DTRs for Alternative Outcome Types -- Inference and Non-regularity -- Additional Considerations and Final Thoughts -- Glossary -- Index -- References Statistical Methods for Dynamic Treatment Regimes shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which is a medical paradigm that emphasizes the systematic use of individual patient information to optimize patient health care. This is the first single source to provide an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. The first chapter establishes context for the statistical reader in the landscape of personalized medicine. Readers need only have familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowledge of statistical programming could implement the methods from scratch. This will be an important volume for a wide range of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications. Advanced graduate students in statistics and biostatistics will also find material in Statistical Methods for Dynamic Treatment Regimes to be a critical part of their studies HTTP:URL=https://doi.org/10.1007/978-1-4614-7428-9

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