Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine (Paperback)

Michael R. Kosorok, Erica E. M. Moodie

Edité par Society for Industrial Applied Mathematics,U.S., 2016
ISBN 10: 1611974178 / ISBN 13: 9781611974171
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Language: English . Brand New Book. Personalized medicine is a medical paradigm that emphasizes systematic use of individual patient information to optimize that patient s health care, particularly in managing chronic conditions and treating cancer. In the statistical literature, sequential decision making is known as an adaptive treatment strategy (ATS) or a dynamic treatment regime (DTR). The field of DTRs emerges at the interface of statistics, machine learning and biomedical science to provide a data-driven framework for precision medicine. A learning-by-seeing approach to the development of ATSs is provided in this book. While estimation procedures are described in sufficient heuristic and technical detail, so that less quantitative readers can understand the broad principles underlying the approaches, practices can also be implemented by more quantitative readers. As the most up-to-date summary of the current state of statistical research in personalized medicine, this book is ideal for a broad audience of health researchers. N° de réf. du libraire

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Synopsis : Personalized medicine is a medical paradigm that emphasizes systematic use of individual patient information to optimize that patient's health care, particularly in managing chronic conditions and treating cancer. In the statistical literature, sequential decision making is known as an adaptive treatment strategy (ATS) or a dynamic treatment regime (DTR). The field of DTRs emerges at the interface of statistics, machine learning, and biomedical science to provide a data-driven framework for precision medicine.

The authors provide a learning-by-seeing approach to the development of ATSs, aimed at a broad audience of health researchers. All estimation procedures used are described in sufficient heuristic and technical detail so that less quantitative readers can understand the broad principles underlying the approaches. At the same time, more quantitative readers can implement these practices.

Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine provides the most up-to-date summary of the current state of the statistical research in personalized medicine; contains chapters by leaders in the area from both the statistics and computer sciences fields; and also contains a range of practical advice, introductory and expository materials, and case studies.

The authors multidisciplinary approach unifies the subject for practicing statisticians, medical and public health researchers, and computer scientists interested in medical applications. Graduate students in all these fields will find both theory and practice in the book, including real-world case studies.

Contents: Chapter 1: Introduction; Part I: Design of Trials for Estimating Dynamic Treatment Regimes; Chapter 2: DTRs and SMARTs: Definitions, designs, and applications; Chapter 3: Efficient design for clinically relevant intent-to-treat comparisons; Chapter 4: SMART design, conduct, and analysis in oncology; Chapter 5: Sample size calculations for clustered SMART designs; Part II: Practical Challenges in Dynamic Treatment Regime Analyses; Chapter 6: Analysis in the single-stage setting: An overview of estimation approaches for dynamic treatment regimes; Chapter 7: G-estimation for dynamic treatment regimes in the longitudinal setting; Chapter 8: Outcome weighted learning methods for optimal dynamic treatment regimes; Chapter 9: Value search estimators for optimal dynamic treatment regimes; Chapter 10: Evaluation of longitudinal dynamics with and without marginal structural working models; Chapter 11: Imputation strategy for SMARTs; Chapter 12: Clinical trials for personalized dose finding; Chapter 13: Methods for analyzing DTRs with censored survival data; Chapter 14: Outcome weighted learning with a reject option; Chapter 15: Estimation of dynamic treatment regimes for complex outcomes: Balancing benefits and risks; Chapter 16: Practical reinforcement learning in dynamic treatment regimes; Chapter 17: Reinforcement learning applications in clinical trials; Bibliography; Index.

Book Description: The most up-to-date summary of current statistical research in personalized medicine, ideal for a broad range of medical researchers. While estimation procedures are described in sufficient heuristic and technical detail so less quantitative readers can understand broad principles underlying the approaches, practices can also be implemented by more quantitative readers.

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Titre : Adaptive Treatment Strategies in Practice: ...
Éditeur : Society for Industrial Applied Mathematics,U.S.
Date d'édition : 2016
Reliure : Paperback
Etat du livre : New

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Erica E. M. Moodie, Michael R. Kosorok
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ISBN 10 : 1611974178 ISBN 13 : 9781611974171
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Erica E. M. Moodie, Michael R. Kosorok
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Michael R. Kosorok; Erica E. M. Moodie
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Description du livre Society for Industrial Applied Mathematics,U.S., United States, 2016. Paperback. État : New. Language: English . Brand New Book. Personalized medicine is a medical paradigm that emphasizes systematic use of individual patient information to optimize that patient s health care, particularly in managing chronic conditions and treating cancer. In the statistical literature, sequential decision making is known as an adaptive treatment strategy (ATS) or a dynamic treatment regime (DTR). The field of DTRs emerges at the interface of statistics, machine learning, and biomedical science to provide a data-driven framework for precision medicine. The authors provide a learning-by-seeing approach to the development of ATSs, aimed at a broad audience of health researchers. All estimation procedures used are described in sufficient heuristic and technical detail so that less quantitative readers can understand the broad principles underlying the approaches. At the same time, more quantitative readers can implement these practices. This book:* Provides the most up-to-date summary of the current state of the statistical research in personalized medicine.* Contains chapters by leaders in the area from both the statistics and computer sciences fields.*Contains a range of practical advice, introductory and expository materials, and case studies. N° de réf. du libraire LIB9781611974171

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Edité par Society for Industrial Applied Mathematics,U.S., United States (2016)
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Description du livre Society for Industrial Applied Mathematics,U.S., United States, 2016. Paperback. État : New. Language: English . Brand New Book. Personalized medicine is a medical paradigm that emphasizes systematic use of individual patient information to optimize that patient s health care, particularly in managing chronic conditions and treating cancer. In the statistical literature, sequential decision making is known as an adaptive treatment strategy (ATS) or a dynamic treatment regime (DTR). The field of DTRs emerges at the interface of statistics, machine learning, and biomedical science to provide a data-driven framework for precision medicine. The authors provide a learning-by-seeing approach to the development of ATSs, aimed at a broad audience of health researchers. All estimation procedures used are described in sufficient heuristic and technical detail so that less quantitative readers can understand the broad principles underlying the approaches. At the same time, more quantitative readers can implement these practices. This book:* Provides the most up-to-date summary of the current state of the statistical research in personalized medicine.* Contains chapters by leaders in the area from both the statistics and computer sciences fields.*Contains a range of practical advice, introductory and expository materials, and case studies. N° de réf. du libraire LIB9781611974171

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Michael R. Kosorok, Erica E. M. Moodie
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Michael R. Kosorok
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Description du livre Cambridge University Pr. Jan 2016, 2016. Taschenbuch. État : Neu. Neuware - The most up-to-date summary of current statistical research in personalized medicine, ideal for a broad audience of medical researchers. 348 pp. Englisch. N° de réf. du libraire 9781611974171

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Michael R. Kosorok
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Description du livre Cambridge University Pr. Jan 2016, 2016. Taschenbuch. État : Neu. Neuware - The most up-to-date summary of current statistical research in personalized medicine, ideal for a broad audience of medical researchers. 348 pp. Englisch. N° de réf. du libraire 9781611974171

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