Bayesian Analysis with R for Drug Development : Concepts, Algorithms, and Case Studies

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 Bayesian applications using real-life examples
- Includes easy-to-follow R code with Bayesian Markov Chain Monte Carlo performed in both JAGS and Stan Bayesian software platforms
- Offers sufficient background for each problem and detailed description of solutions suitable for practitioners with limited Bayesian knowledge