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

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

Authors: Harry Yang, Steven Novick