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Statistical Methods for Dynamic Treatment Regimes

Reinforcement Learning, Causal Inference, and Personalized Medicine

  • Bibhas Chakraborty
  • Erica E.M. Moodie

Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Bibhas Chakraborty, Erica E. M. Moodie
    Pages 1-8
  3. Bibhas Chakraborty, Erica E. M. Moodie
    Pages 9-30
  4. Bibhas Chakraborty, Erica E. M. Moodie
    Pages 31-52
  5. Bibhas Chakraborty, Erica E. M. Moodie
    Pages 79-100
  6. Bibhas Chakraborty, Erica E. M. Moodie
    Pages 101-112
  7. Bibhas Chakraborty, Erica E. M. Moodie
    Pages 113-125
  8. Bibhas Chakraborty, Erica E. M. Moodie
    Pages 127-168
  9. Bibhas Chakraborty, Erica E. M. Moodie
    Pages 169-180
  10. Back Matter
    Pages 181-204

About this book

Introduction

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.

Keywords

Causal inference Dynamic treatments Personalized medicine Reinforcement learning Statistical methods Treatment

Authors and affiliations

  • Bibhas Chakraborty
    • 1
  • Erica E.M. Moodie
    • 2
  1. 1., Department of BiostatisticsColumbia UniversityNew YorkUSA
  2. 2., Department of EpidemiologyMcGill UniversityMontrealCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-7428-9
  • Copyright Information Springer Science+Business Media New York 2013
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-7427-2
  • Online ISBN 978-1-4614-7428-9
  • Series Print ISSN 1431-8776
  • Buy this book on publisher's site