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LEADER 00000cam 2200529Ki 4500
006 m o d
007 cr cnu|||unuuu
008 130826s2013 nyua ob 001 0 eng d
020 9781461474289|q(electronic bk.)
020 1461474280|q(electronic bk.)
024 7 10.1007/978-1-4614-7428-9
050 4 RA409
072 7 PBT|2bicssc
072 7 MBNS|2bicssc
072 7 MED090000|2bisacsh
082 04 610.72/7|223
100 1 Chakraborty, Bibhas,|eauthor.
245 10 Statistical methods for dynamic treatment regimes :
|breinforcement learning, causal inference, and
personalized medicine /|cBibhas Chakraborty, Erica E.M.
264 1 New York, NY :|bSpringer,|c2013.
300 1 online resource (xvi, 204 pages) :|billustrations.
338 online resource|bcr|2rdacarrier
490 1 Statistics for Biology and Health,|x1431-8776
504 Includes bibliographical references and index.
505 0 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.
520 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.
588 Description based on online resource; title from PDF title
page (SpringerLink, viewed August 20, 2013).
650 0 Medical statistics.
650 0 Medical records|xData processing.
655 4 Electronic books.
700 1 Moodie, Erica E. M.|eauthor.
830 0 Statistics for biology and health,|x1431-8776
856 40 |uhttp://dx.doi.org.ezp.lib.unimelb.edu.au/10.1007/978-1-
4614-7428-9|zConnect to ebook (University of Melbourne
938 YBP Library Services|bYANK|n11059595
990 Ebook load - do not edit, delete or attach any records.