Description
Book SynopsisDrug 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
- Co
Table of Contents
Background. Drug Research and Development. Basics of Bayesian analysis. Bayesian Estimation of Sample Size and Power. Pre-Clinical and Clinical Research. Pre-clinical efficacy study. Futility analysis. Phase 3 Clinical Trial. Chemistry, Manufacturing, and Control. Analytical method. Process Development. Bayesian Approach to Statistical Process Control.
