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LEADER 00000cam  2200529Ki 4500 
003    OCoLC 
005    20131008081018.0 
006    m     o  d         
007    cr cnu|||unuuu 
008    130826s2013    nyua    ob    001 0 eng d 
019    SPRINGERocn856902925 
020    9781461474289|q(electronic bk.) 
020    1461474280|q(electronic bk.) 
020    |z9781461474272 
024 7  10.1007/978-1-4614-7428-9 
035    (OCoLC)856902925 
040    GW5XE|erda|epn|cGW5XE|dYDXCP 
049    UMVA 
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. 
       Moodie. 
264  1 New York, NY :|bSpringer,|c2013. 
300    1 online resource (xvi, 204 pages) :|billustrations. 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
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 
       only) 
938    YBP Library Services|bYANK|n11059595 
990    Ebook load  - do not edit, delete or attach any records. 
994    92|bUMV 
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