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Books > Medicine > General issues > Public health & preventive medicine > Epidemiology & medical statistics
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Bayesian Analysis with R for Drug Development - Concepts, Algorithms, and Case Studies (Hardcover)
Loot Price: R2,906
Discovery Miles 29 060
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Bayesian Analysis with R for Drug Development - Concepts, Algorithms, and Case Studies (Hardcover)
Series: Chapman & Hall/CRC Biostatistics Series
Expected to ship within 12 - 19 working days
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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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