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020 _a9781461474289
_9978-1-4614-7428-9
024 7 _a10.1007/978-1-4614-7428-9
_2doi
049 _aAlfaisal Main Library
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMBNS
_2bicssc
072 7 _aMED090000
_2bisacsh
082 0 4 _a519.5
_223
100 1 _aChakraborty, Bibhas.
_eauthor.
245 1 0 _aStatistical Methods for Dynamic Treatment Regimes
_h[electronic resource] :
_bReinforcement Learning, Causal Inference, and Personalized Medicine /
_cby Bibhas Chakraborty, Erica E.M. Moodie.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _aXVI, 204 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStatistics for Biology and Health,
_x1431-8776
505 0 _aIntroduction -- 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.
520 _aStatistical 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.
650 0 _aStatistics.
650 0 _aHealth informatics.
650 1 4 _aStatistics.
650 2 4 _aStatistics for Life Sciences, Medicine, Health Sciences.
650 2 4 _aStatistics, general.
650 2 4 _aHealth Informatics.
655 7 _aElectronic books.
_2local
700 1 _aMoodie, Erica E.M.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461474272
830 0 _aStatistics for Biology and Health,
_x1431-8776
856 4 0 _uhttp://ezproxy.alfaisal.edu/login?url=http://dx.doi.org/10.1007/978-1-4614-7428-9
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