 Название: Bayesian Analysis with R for Drug Development : Concepts, Algorithms, and Case Studies
Автор: Harry Yang, Steven Novick
Издательство: Chapman and Hall/CRC
Год: 2019
Страниц: 327
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
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
Although several Bayesian books have been published to address a broad array of drug development problems, they are primarily focused on clinical trial design, safety analysis, observational studies, and cost-effectiveness assessment. Bayesian methodologies remain unfamiliar to the majority of statistical practitioners in the non-clinical areas. Suffice to say, the lack of adoption of Bayesian methods in those non-clinical areas, including drug discovery, analytical method development, process optimization, and manufacturing control has resulted in many missed opportunities for statisticians to make meaningful differences. Neither is this in keeping with the recent regulatory initiatives of quality by design (QbD), which achieves product quality through greater understanding of the product and manufacturing process, based on knowledge and data collected throughout the lifecycle of the product development.
It is the desire to fill the aforesaid gap that motivates us to write this book. The aim of this book is to provide Bayesian applications to a wide range of clinical and non-clinical issues in drug development. Each Bayesian method in the book is used to address a specific scientific question and illustrated through a case study. The R code used for implementing the method is discussed and included. It is our belief that the publication of this book will promote the use of Bayesian approaches in pharmaceutical practices.
The book consists of three parts, totaling 11 chapters. Since the primary aim of this book is to use case studies, examples, and easy-to-follow R code to demonstrate Bayesian applications in the entire spectrum of drug development, it is, by no means, meant to be comprehensive in literature review, nor is it intended to be exhaustive in expounding each application.
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Bayesian Analysis with R for Drug Development: Concepts, Algorithms, and Case Studies 
