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.
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 Quebec-Sante. Her main research interests are in causal inference and longitudinal data, with a focus on dynamic treatment regimes.
* 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.