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Discover the Latest Statistical Approaches for Modeling ExposureResponse RelationshipsWritten by an applied statistician with extensive practical experience in drug development, ExposureResponse Modeling: Methods and Practical Implementation explores a wide range of topics in exposureresponse modeling, from traditional pharmacokineticpharmacodynamic (PKPD) modeling to other areas in drug development and beyond. It incorporates numerous examples and software programs for implementing novel methods.The book describes using measurement error models to treat sequential modeling, fitting models with exposure and response driven by complex dynamics, and survival analysis with dynamic exposure history. It also covers Bayesian analysis and modelbased Bayesian decision analysis, causal inference to eliminate confounding biases, and exposureresponse modeling with responsedependent dose/treatment adjustments (dynamic treatment regimes) for personalized medicine and treatment adaptation.Many examples illustrate the use of exposureresponse modeling in experimental toxicology, clinical pharmacology, epidemiology, and drug safety. Some examples demonstrate how to solve practical problems while others help with understanding concepts and evaluating the performance of new methods. The provided SAS and R codes enable readers to test the approaches in their own scenarios.Although application oriented, this book also gives a systematic treatment of concepts and methodology. Applied statisticians and modelers can find details on how to implement new approaches. Researchers can find topics for or applications of their work. In addition, students can see how complicated methodology and models are applied to practical situations.
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