9781498769778-1498769772-Dynamic Treatment Regimes: Statistical Methods for Precision Medicine (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Dynamic Treatment Regimes: Statistical Methods for Precision Medicine (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

ISBN-13: 9781498769778
ISBN-10: 1498769772
Edition: 1
Author: Tsiatis, Anastasios A., Davidian, Marie, Holloway, Shannon T., Laber, Eric B.
Publication date: 2019
Publisher: Chapman and Hall/CRC
Format: Hardcover 618 pages
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Book details

ISBN-13: 9781498769778
ISBN-10: 1498769772
Edition: 1
Author: Tsiatis, Anastasios A., Davidian, Marie, Holloway, Shannon T., Laber, Eric B.
Publication date: 2019
Publisher: Chapman and Hall/CRC
Format: Hardcover 618 pages

Summary

Acknowledged authors Tsiatis, Anastasios A., Davidian, Marie, Holloway, Shannon T., Laber, Eric B. wrote Dynamic Treatment Regimes: Statistical Methods for Precision Medicine (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) comprising 618 pages back in 2019. Textbook and eTextbook are published under ISBN 1498769772 and 9781498769778. Since then Dynamic Treatment Regimes: Statistical Methods for Precision Medicine (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) textbook was available to sell back to BooksRun online for the top buyback price of $ 4.04 or rent at the marketplace.

Description

Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for the evaluation and discovery of dynamic treatment regimes from data. Researchers and graduate students in statistics, data science, and related quantitative disciplines with a background in probability and statistical inference and popular statistical modeling techniques will be prepared for further study of this rapidly evolving field.

A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key decision point in a disease or disorder process, where each rule takes as input patient information and returns the treatment option he or she should receive. Thus, a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice. Treatment regimes are of obvious relevance to precision medicine, which involves tailoring treatment selection to patient characteristics in an evidence-based way. Of critical importance to precision medicine is estimation of an optimal treatment regime, one that, if used to select treatments for the patient population, would lead to the most beneficial outcome on average. Key methods for estimation of an optimal treatment regime from data are motivated and described in detail. A dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors.

The authors’ website www.dtr-book.com includes updates, corrections, new papers, and links to useful websites.

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