1st Edition
Bayesian Methods in Pharmaceutical Research
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.
I Introductory Part
Chapter 1: Bayesian Background
Emmanuel Lesaffre and Gianluca Baio
Chapter 2: FDA Regulatory Acceptance of Bayesian Statistics
Gregory Campbell
Chapter 3: Bayesian Tail Probabilities for Decision Making
Leonhard Held
II Clinical Development
Chapter 4: Clinical Development in the Light of Bayesian Statistics
David Ohlssen
Chapter 5: Prior Elicitation
Nicky Best, Nigel Dallow, and Timothy Montague
Chapter 6: Use of Historical Data
Beat Neuenschwander and Heinz Schmidli
Chapter 7: Dose Ranging Studies and Dose Determination
Phil Woodward, Alun Bedding, and David Dejardin
Chapter 8: Bayesian Adaptive Designs in Drug Development
Gary L. Rosner
Chapter 9: Bayesian Methods for Longitudinal Data with Missingness
Michael J. Daniels and Dandan Xu
Chapter 10: Survival Analysis and Censored Data
Linda D. Sharples and Nikolaos Demiris
Chapter 11: Benefit of Bayesian Clustering of Longitudinal Data: Study of Cognitive Decline for Precision Medicine
Anais Rouanet, Sylvia Richardson, and Brian Tom
Chapter 12: Bayesian Frameworks for Rare Disease Clinical Development Programs
Freda Cooner, Forrest Williamson, and Bradley P. Carlin
Chapter 13: Bayesian Hierarchical Models for Data Extrapolation and Analysis in Pediatric Disease Clinical Trials
Cynthia Basu and Bradley P. Carlin
III Post-Marketing
Chapter 14: Bayesian Methods for Meta-Analysis
Nicky J Welton, Haley E Jones, and Sofia Dias
Chapter 15: Economic Evaluation and Cost-Effectiveness of Health Care Interventions
Nicky J Welton, Mark Strong, Christopher Jackson, and Gianluca Baio
Chapter 16: Bayesian Modeling for Economic Evaluation Using "Real World Evidence"
Gianluca Baio
Chapter 17: Bayesian Benefit-Risk Evaluation in Pharmaceutical Research
Carl Di Casoli, Yueqin Zhao, Yannis Jemiai, Pritibha Singh, and Maria Costa
IV Product Development and Manufacturing
Chapter 18: Product Development and Manufacturing
Bruno Boulanger and Timothy Mutsvari
Chapter 19: Process Development and Validation
John J. Peterson
Chapter 20: Analytical Method and Assay
Pierre Lebrun and Eric Rozet
Chapter 21: Bayesian Methods for the Design and Analysis of Stability Studies
Tonakpon Hermane Avohou, Pierre Lebrun, Eric Rozet, and Bruno Boulanger
Chapter 22: Content Uniformity Testing
Steven Novick and Buffy Hudson-Curtis
Chapter 23: Bayesian methods for in vitro dissolution drug testing and similarity comparisons
Linas Mockus and Dave LeBlond
Chapter 24: Bayesian Statistics for Manufacturing
Tara Scherder and Katherine Giacoletti
V Additional Topics
Chapter 25: Bayesian Statistical Methodology in the Medical Device Industry
Tarek Haddad
Chapter 26: Program and Portfolio Decision-Making
Nitin Patel, Charles Liu, Masanori Ito, Yannis Jemiai, Suresh Ankolekar, and Yusuke Yamaguchi
Biography
Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger