Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies (Chapman & Hall/CRC Biostatistics Series)

Drug development is an iterative process. The recent publications of regulatory guidelines further entail a lifecycle approach.Blending data from disparate sources, the Bayesian approach provides a flexible framework for drug development.Despite its advantages, the uptake of Bayesian methodologies is lagging behind in the field of pharmaceutical development. Written specifically for pharmaceutical practitioners, Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies, describes a wide range of Bayesian applications to problems throughout pre-clinical, clinical, and Chemistry, Manufacturing, and Control (CMC) development.Authored by two seasoned statisticians in the pharmaceutical industry, the book provides detailed Bayesian solutions to a broad array of pharmaceutical problems. Features Provides a single source of information on Bayesian statistics for drug development Covers a wide spectrum of pre-clinical, clinical, and CMC topics Demonstrates proper Ba