Bayesian Methods in Pharmaceutical Research (Chapman & Hall/CRC Biostatistics Series) (Original PDF from Publisher)

By Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger

Since the early 2000s, there has been increasing interest within the pharmaceutical industry in the application of Bayesian methods at various stages of the research, development, manufacturing, and health economic evaluation of new health care interventions. In 2010, the first Applied Bayesian Biostatistics conference was held, with the primary objective to stimulate the practical implementation of Bayesian statistics, and to promote the added-value for accelerating the discovery and the delivery of new cures to patients.

This book is a synthesis of the conferences and debates, providing an overview of Bayesian methods applied to nearly all stages of research and development, from early discovery to portfolio management. It highlights the value associated with sharing a vision with the regulatory authorities, academia, and pharmaceutical industry, with a view to setting up a common strategy for the appropriate use of Bayesian statistics for the benefit of patients.

The book covers:

• Theory, methods, applications, and computing
• Bayesian biostatistics for clinical innovative designs
• Adding value with Real World Evidence
• Opportunities for rare, orphan diseases, and pediatric development
• Applied Bayesian biostatistics in manufacturing
• Decision making and Portfolio management
• Regulatory perspective and public health policies

Statisticians and data scientists involved in the research, development, and approval of new cures will be inspired by the possible applications of Bayesian methods covered in the book. The methods, applications, and computational guidance will enable the reader to apply Bayesian methods in their own pharmaceutical research.

Product Details

• Publisher ‏ : ‎ Chapman and Hall/CRC; 1st edition (December 13, 2021)
• Language ‏ : ‎ English
• Paperback ‏ : ‎ 546 pages
• ISBN-10 ‏ : ‎ 1032241527
• ISBN-13 ‏ : ‎ 978-1032241524