Dynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for evaluation and discovery of dynamic treatment regimes from data. Methodological developments in this area are scattered across a vast, diverse literature, making this topic difficult to approach. This book addresses this challenge by presenting foundational material in this area in a unified way, offering researchers and graduate students in statistics, data science, and related quantitative disciplines a systematic overview that will serve as a strong basis for further study of this rapidly evolving field.
A dynamic treatment regime is a set of sequential decision rules, each corresponding to a key decision point in a disease or disorder process and taking as input patient information and returning the treatment option he or she should receive. Thus, a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice and is of obvious relevance to precision medicine, which involves tailoring treatment selection to patient characteristics in an evidence-based way. Of critical importance to precision medicine is estimation of an optimal treatment regime, one that, if used to select treatments for the patient population, would lead to the most beneficial outcome on average. Key methods for estimation of an optimal treatment regime from data are motivated and described in detail, and a dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors. Essential aspects are presented at both a less technical and more formal, theoretical level, allowing readers to tailor coverage of the material to their goals and backgrounds.
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