Statistical Methods for Dynamic Treatment Regimes

Reinforcement Learning, Causal Inference, and Personalized Medicine

Nonfiction, Health & Well Being, Medical, Reference, Biostatistics, Science & Nature, Mathematics, Statistics
Cover of the book Statistical Methods for Dynamic Treatment Regimes by Bibhas Chakraborty, Erica E.M. Moodie, Springer New York
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Bibhas Chakraborty, Erica E.M. Moodie ISBN: 9781461474289
Publisher: Springer New York Publication: July 23, 2013
Imprint: Springer Language: English
Author: Bibhas Chakraborty, Erica E.M. Moodie
ISBN: 9781461474289
Publisher: Springer New York
Publication: July 23, 2013
Imprint: Springer
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Springer New York

Cover of the book Ordinary Differential Equations by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Astrolinguistics by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Reviews of Environmental Contamination and Toxicology by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Residue Reviews by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Atomic Scale Characterization and First-Principles Studies of Si₃N₄ Interfaces by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Mercury's Interior, Surface, and Surrounding Environment by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Quantile-Based Reliability Analysis by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Oceanography by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Current Research in Acupuncture by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Informal Introduction to Stochastic Processes with Maple by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Research on e-Learning and ICT in Education by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Ultrasound of the Thyroid and Parathyroid Glands by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Semiparametric and Nonparametric Methods in Econometrics by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book Color Atlas of Fetal and Neonatal Histology by Bibhas Chakraborty, Erica E.M. Moodie
Cover of the book High-Level Verification by Bibhas Chakraborty, Erica E.M. Moodie
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy