2016 | ISBN: 1611974178 | English | 364 Pages | PDF | 7 MB
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.
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