Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine

Overview

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...

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Overview

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.

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Editorial Reviews

Doody's Review Service
Reviewer: Parthiv Amin, MD (East Tennessee State University Quillen College of Medicine)
Description: This novel book provides an introduction to the various statistical methods that define the development of dynamic treatment regime, an important component of the newly evolving idea of personalized medicine. The chapter authors are experts in their fields and take a keen interest in developing the concept of personalized medicine.
Purpose: The purpose is to focus on personalized medicine and various study designs as well as statistical methods to develop evidence-based, personalized treatments for patients with chronic disease. It provides a comprehensive overview of dynamic treatment regimes, which is an important segment of personalized medicine. Ultimately, it has been shown that personalized medicine will improve patient compliance and improve outcomes of treatment for various chronic diseases.
Audience: The intended audience includes physicians, clinical researchers, physicians in training, statisticians, and medical students, as well as master's and doctoral students in the field of biostatistics and epidemiology and computer scientists.
Features: The book is well structured with the initial two chapters providing an introduction to personalized medicine and dynamic treatment regimens. The subsequent chapters describe the science of mathematics and various statistical methods. Each chapter has numerous clinically based data examples that precisely explain the different statistical methods. With extensive graphs and tables, the authors have done an excellent job of providing a concise and simple approach to learning complicated statistical methods. The utility of each method as well as a summation is provided in each chapter, which makes conceptualization easy.
Assessment: This book provides a concise summary of the key findings in the statistical literature of dynamic treatment regimes. Personalized medicine is a fast evolving branch of medicine and this book explains the various methods involved in the development of regimes. The simple language and well-organized chapters are unsurpassed attributes of this book. It will be an exceptional resource for quick review.
From the Publisher
From the reviews:

"Overall, the book provides an excellent reviewof DTRs up to date. After finishing reading the book, I planned to create a post-graduate seminar course on this topic using this book as a textbook. I enthusiastically recommend this book. This book will be a valuable reference for anyone interested and involved in research on personalized medicine." (Hyonggin An, Journal of Agricultural, Biological, and Environmental Statistics, April, 2015)

“The intended audience includes physicians, clinical researchers, physicians in training, statisticians, and medical students, as well as master’s and doctoral students in the field of biostatistics and epidemiology and computer scientists. … This book provides a concise summary of the key findings in the statistical literature of dynamic treatment regimes. … The simple language and well-organized chapters are unsurpassed attributes of this book. It will be an exceptional resource for quick review.” (Parthiv Amin, Doody’s Book Reviews, November, 2013)

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Product Details

  • ISBN-13: 9781461474272
  • Publisher: Springer New York
  • Publication date: 6/30/2013
  • Series: Statistics for Biology and Health Series, #76
  • Edition description: 2013
  • Edition number: 1
  • Pages: 204
  • Product dimensions: 6.20 (w) x 9.20 (h) x 0.70 (d)

Meet the Author

Bibhas Chakraborty is an Assistant Professor of Biostatistics at the Mailman School of Public Health, Columbia University. His primary research interests lie in dynamic treatment regimes, machine learning and data mining including reinforcement learning, causal inference, and design and analysis of clinical trials. He received a Bachelor’s degree from the University of Calcutta, a Master’s degree from the Indian Statistical Institute, and a Ph.D. in Statistics from the University of Michigan. He is the recipient of the Calderone Research Prize for Junior Faculty from the Mailman School of Public Health, Columbia University, in 2011.

Erica Moodie is an Associate Professor of Biostatistics in the Department of Epidemiology, Biostatistics, and Occupational Health at McGill University. Her main interests lie in causal inference and longitudinal data with a focus on methods for HIV research. She is an Associate Editor of The International Journal of Biostatistics and Journal of Causal Inference. She received a bachelor's degree in Mathematics and Statistics from the University of Winnipeg, an M.Phil. in Epidemiology from the University of Cambridge, and a Ph.D. in Biostatistics from the University of Washington. She is the recipient of a Natural Sciences and Engineering Research Council University Faculty Award.

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Table of Contents

Introduction.- 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.- Glossary.- Index.- References.

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