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

Book Details

Format Paperback / Softback
ISBN-10 1032177861
ISBN-13 9781032177861
Publisher Taylor & Francis Ltd
Imprint Chapman & Hall/CRC
Country of Manufacture US
Country of Publication GB
Publication Date Sep 30th, 2021
Print length 310 Pages
Weight 494 grams
Dimensions 15.60 x 23.10 x 2.30 cms
Ksh 8,350.00
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Few books deal with issues faced by researchers in the areas of pre-clinical animal studies, analytical method and process development, manufacturing control, the implementation of Bayesian applications for pharmaceutical problems, using modern computational tools such as R. This book does.

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

Harry Yang, Ph.D., is Senior Director and Head of Statistical Sciences at AstraZeneca. He has 24 years of experience across all aspects of drug research and development and extensive global regulatory experiences. He has published 6 statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects, including 15 joint statistical works with Dr. Novick. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP as well as Peking University.

Steven Novick, Ph.D., is Director of Statistical Sciences at AstraZeneca. He has extensively contributed statistical methods to the biopharmaceutical literature. Novick is a skilled Bayesian computer programmer and is frequently invited to speak at conferences, having developed and taught courses in several areas, including drug-combination analysis and Bayesian methods in clinical areas. Novick served on IPAC-RS and has chaired several national statistical conferences.


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