Titlebook: Statistical Methods for Dynamic Treatment Regimes; Reinforcement Learni Bibhas Chakraborty,Erica E.M. Moodie Textbook 2013 Springer Science

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1431-8776 oaches to the development of dynamic treatment regime models.Statistical Methods for Dynamic Treatment Regimes. shares state of the art of statistical methods developed to address questions of estimation and inference for dynamic treatment regimes, a branch of personalized medicine. This volume demo

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Statistical Reinforcement Learning,sequence of treatments. This problem bears strong resemblance to the problem of reinforcement learning in computer science, a branch of machine learning that deals with the problem of multi-stage, sequential decision making by a learning agent. In this chapter, we review the necessary concepts of re

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Estimation of Optimal DTRs by Directly Modeling Regimes,ling the conditional mean outcome: inverse probability of treatment weighting, marginal structural models, and classification-based methods. The fundamental difference between the approaches considered in the current chapter and those considered in previous chapters (e.g. Q-learning and G-estimation

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Inference and Non-regularity,he optimal treatments at subsequent stages are non-unique for at least some strictly positive proportion of subjects in the population. We discuss and illustrate the phenomenon using Q-learning and G-estimation, and propose a number of strategies to mitigate the non-regularity including thresholding

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Statistical Reinforcement Learning,inforcement learning, connect them to the relevant statistical literature, and develop a mathematical framework that will enable us to treat the problem of estimating the optimal dynamic treatment regimes rigorously.

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Textbook 2013ference for dynamic treatment regimes, a branch of personalized medicine. This volume demonstrates these methods with their conceptual underpinnings and illustration through analysis of real and simulated data. These methods are immediately applicable to the practice of personalized medicine, which