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. A learning-by-seeing approach to the development of ATSs is provided in this book. While estimation procedures are described in sufficient heuristic and technical detail, so that less quantitative readers can understand the broad principles underlying the approaches, practices can also be implemented by more quantitative readers. As the most up-to-date summary of the current state of statistical research in personalized medicine, this book is ideal for a broad audience of health researchers.