000 02657 a2200421 4500
001 1032082283
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008 250312042021xx eng
020 _a9781032082288
037 _bTaylor & Francis
_cGBP 45.99
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040 _a01
041 _aeng
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100 1 _aAnastasios A. Tsiatis
245 1 0 _aDynamic Treatment Regimes
_bStatistical Methods for Precision Medicine
250 _a1
260 _bChapman and Hall/CRC
_c20210802
300 _a618 p
520 _bDynamic Treatment Regimes: Statistical Methods for Precision Medicine provides a comprehensive introduction to statistical methodology for the evaluation and discovery of dynamic treatment regimes from data. Researchers and graduate students in statistics, data science, and related quantitative disciplines with a background in probability and statistical inference and popular statistical modeling techniques will be prepared 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, where each rule takes as input patient information and returns the treatment option he or she should receive. Thus, a treatment regime formalizes how a clinician synthesizes patient information and selects treatments in practice. Treatment regimes are 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. A dedicated companion website presents full accounts of application of the methods using a comprehensive R package developed by the authors. The authors’ website www.dtr-book.com includes updates, corrections, new papers, and links to useful websites.
700 1 _aMarie Davidian
_4A01
700 1 _aShannon T. Holloway
_4A01
700 1 _aEric B. Laber
_4A01
999 _c824
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