Adaptive Treatment Strategies in Practice

Planning Trials and Analyzing Data for Personalized Medicine

AvMichael R. Kosorok,Erica E. M. Moodie

Häftad, Engelska, 2015

Del i serien ASA-SIAM Series on Statistics and Applied Probability

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2015-12-30
Mått:152 x 229 x 25 mm
Vikt:783 g
Format:Häftad
Språk:Engelska
Serie:ASA-SIAM Series on Statistics and Applied Probability
Antal sidor:364
Förlag:Society for Industrial & Applied Mathematics,U.S.
ISBN:9781611974171

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Mer om författaren

Michael R. Kosorok is W. R. Kenan, Jr Distinguished Professor and Chair of Biostatistics and Professor of Statistics and Operations Research at the University of North Carolina, Chapel Hill. He is an honorary fellow of both the American Statistical Association and the Institute of Mathematical Statistics and an Associate Editor of The Annals of Statistics, the Journal of the American Statistical Association, and the Journal of the Royal Statistical Society, Series B. He is the contact principal investigator for a program project (P01) from the US National Cancer Institute, entitled 'Statistical Methods for Cancer Clinical Trials'. His main research interests are in precision medicine, clinical trials, machine learning, and related areas. Erica E. M. Moodie is a William Dawson Scholar and an Associate Professor of Biostatistics in the Department of Epidemiology, Biostatistics and Occupational Health at McGill University. She is an Elected Member of the International Statistical Institute, and an Associate Editor of Biometrics and the Journal of the American Statistical Association. She holds a Chercheur–Boursier Junior 2 career award from the Fonds de Recherche du Québec-Santé. Her main research interests are in causal inference and longitudinal data, with a focus on dynamic treatment regimes.

Innehållsförteckning

Chapter 1: IntroductionPart I: Design of Trials for Estimating Dynamic Treatment RegimesChapter 2: DTRs and SMARTs: Definitions, designs, and applicationsChapter 3: Efficient design for clinically relevant intent-to-treat comparisonsChapter 4: SMART design, conduct, and analysis in oncologyChapter 5: Sample size calculations for clustered SMART designsPart II: Practical Challenges in Dynamic Treatment Regime AnalysesChapter 6: Analysis in the single-stage setting: An overview of estimation approaches for dynamic treatment regimes.