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" Adaptive treatment strategies in practice : "
edited by Michael Kosorok, Erica Moodie.
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BL
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| Record Number
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854430
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| Title & Author
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Adaptive treatment strategies in practice : : planning trials and analyzing data for personalized medicine /\ edited by Michael Kosorok, Erica Moodie.
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| Publication Statement
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Philadelphia, Pennsylvania :: Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104),, [2015]
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| Series Statement
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ASA-SIAM statistics and applied probability series
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| Page. NO
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1 online resource (xvi, 348 pages).
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| ISBN
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1611974186
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: 9781611974188
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9781611974171
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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Preface -- 1. Introduction -- part I. Design of trials for estimating dynamic treatment regimes -- 2. DTRs and SMARTs : definitions, designs, and applications -- 3. Efficient design for clinically relevant intent-to-treat comparisons -- 4. SMART design, conduct, and analysis in oncology -- 5. Sample size calculations for clustered SMART designs -- part II. Practical challenges in dynamic treatment regime analyses -- 6. Analysis in the single-stage setting : an overview of estimation approaches for dynamic treatment regimes -- 7. G-estimation for dynamic treatment regimes in the longitudinal setting -- 8. Outcome weighted learning methods for optimal dynamic treatment regimes -- 9. Value search estimators for optimal dynamic treatment regimes -- 10. Evaluation of longitudinal dynamics with and without marginal structural working models -- 11. Imputation strategy for SMARTs -- 12. Clinical trials for personalized dose finding -- 13. Methods for analyzing DTRs with censored survival data -- 14. Outcome weighted learning with a reject option -- 15. Estimation of dynamic treatment regimes for complex outcomes : balancing benefits and risks -- 16. Practical reinforcement learning in dynamic treatment regimes -- 17. Reinforcement learning applications in clinical trials.
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| Abstract
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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, contains a range of practical advice, introductory and expository materials, and case studies.
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| Subject
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Clinical trials-- Statistical methods.
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Medical statistics.
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Medicine-- Research-- Statistical methods.
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Clinical trials-- Statistical methods.
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Medical statistics.
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Medicine-- Research-- Statistical methods.
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| Dewey Classification
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610.2/1
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| LC Classification
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RA409.A33 2015eb
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| Added Entry
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Kosorok, Michael R.
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Moodie, Erica E. M.
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Society for Industrial and Applied Mathematics,publisher.
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