By Jixian Wang
Discover the most recent Statistical methods for Modeling Exposure-Response Relationships
Written by way of an utilized statistician with large sensible adventure in drug improvement, Exposure-Response Modeling: equipment and useful Implementation explores quite a lot of issues in exposure-response modeling, from conventional pharmacokinetic-pharmacodynamic (PKPD) modeling to different parts in drug improvement and past. It comprises a variety of examples and software program courses for imposing novel methods.
The booklet describes utilizing size blunders versions to regard sequential modeling, becoming versions with publicity and reaction pushed through advanced dynamics, and survival research with dynamic publicity historical past. It additionally covers Bayesian research and model-based Bayesian determination research, causal inference to dispose of confounding biases, and exposure-response modeling with response-dependent dose/treatment alterations (dynamic remedy regimes) for customized drugs and therapy adaptation.
Many examples illustrate using exposure-response modeling in experimental toxicology, medical pharmacology, epidemiology, and drug safeguard. a few examples show the way to resolve sensible difficulties whereas others support with realizing innovations and comparing the functionality of recent tools. The supplied SAS and R codes allow readers to check the ways of their personal situations.
Although program orientated, this publication additionally supplies a scientific therapy of ideas and method. utilized statisticians and modelers can locate information on the best way to enforce new techniques. Researchers can locate subject matters for or purposes in their paintings. additionally, scholars can see how advanced technique and versions are utilized to useful situations.
Read or Download Exposure–Response Modeling: Methods and Practical Implementation (Chapman & Hall/CRC Biostatistics Series) PDF
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Exposure–Response Modeling: Methods and Practical Implementation (Chapman & Hall/CRC Biostatistics Series) by Jixian Wang
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