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Cover Art
Author Chakraborty, Bibhas, author.

Title Statistical methods for dynamic treatment regimes : reinforcement learning, causal inference, and personalized medicine / Bibhas Chakraborty, Erica E.M. Moodie.

Published New York, NY : Springer, 2013.


Location Call No. Status
Physical description 1 online resource (xvi, 204 pages) : illustrations.
Series Statistics for Biology and Health, 1431-8776
Statistics for biology and health, 1431-8776
Contents 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.
Bibliography Includes bibliographical references and index.
Summary 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.
Notes Description based on online resource; title from PDF title page (SpringerLink, viewed August 20, 2013).
Other author Moodie, Erica E. M. author.
Subject Medical statistics.
Medical records -- Data processing.
Electronic books.
ISBN 9781461474289 (electronic bk.)
1461474280 (electronic bk.)
Standard Number 10.1007/978-1-4614-7428-9