Presents statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. These methods are demonstrated with their conceptual underpinnings and illustration through analysis of real and simulated data, and their application to the practice of personalized medicine, which emphasizes the systematic use of individual patient information to optimize patient health care. Provides an overview of methodology and results gathered from journals, proceedings, and technical reports with the goal of orienting researchers to the field. Readers need familiarity with elementary calculus, linear algebra, and basic large-sample theory to use this text. Throughout the text, authors direct readers to available code or packages in different statistical languages to facilitate implementation. In cases where code does not already exist, the authors provide analytic approaches in sufficient detail that any researcher with knowledge of statistical programming could implement the methods from scratch. Applicable to a wide range of researchers, including statisticians, epidemiologists, medical researchers, and machine learning researchers interested in medical applications, as well as advanced graduate students in statistics and biostatistics. Introduction -- The data : observational studies and sequentially randomized trials -- Statistical reinforcement learning -- Semi-parametric estimation of optimal DTRs by modeling contrasts of conditional mean outcomes -- Estimation of optimal DTRs by directly modeling regimes -- G-computation: parametric estimation of optimal DTRs -- Estimation DTRs for alternative outcome types -- Inference and non-regularity -- Additional considerations and final thoughts.
Chakraborty, B., & Moodie, E. E. M. (2013). Statistical Methods for Dynamic Treatment Regimes (p. 204). New York, NY: Springer New York. Retrieved from http://link.springer.com/10.1007/978-1-4614-7428-9