Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine
Adaptive Treatment Strategies in Practice: Planning Trials and Analyzing Data for Personalized Medicine
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实践中的适应性治疗策略：规划试验和分析个性化医疗数据
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
E. Moodie
中科院分区：
文献类型：
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作者：
M. Kosorok;E. Moodie
个性化医疗是一种医学范式，强调系统地使用个体患者信息来优化患者的卫生保健，特别是在管理慢性病和治疗癌症方面。在统计文献中，顺序决策被称为适应性治疗策略（ATS）或动态治疗方案（DTR）。dtr领域出现在统计学，机器学习和生物医学科学的界面，为精准医疗提供数据驱动的框架。作者提供了一种通过观察来学习的方法来开发ats，针对的是广泛的卫生研究人员。所有使用的评估过程都以充分的启发式和技术细节进行了描述，以便较少定量的读者可以理解这些方法背后的广泛原则。同时，更多的量化读者可以实施这些做法。实践中的适应性治疗策略：个性化医疗的计划试验和数据分析提供了个性化医疗统计研究现状的最新总结；包含由领导人的章节从统计和计算机科学领域的领域；并且还包含了一系列实用的建议，介绍性和说明性材料，以及案例研究。作者的多学科方法为实践统计学家、医学和公共卫生研究人员以及对医学应用感兴趣的计算机科学家统一了主题。所有这些领域的研究生都可以在书中找到理论和实践，包括现实世界的案例研究。
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.