1st Edition

Bayesian Methods in Pharmaceutical Research

Edited By Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger Copyright 2020
    546 Pages 111 B/W Illustrations
    by Chapman & Hall

    546 Pages 111 B/W Illustrations
    by Chapman & Hall

    546 Pages 111 B/W Illustrations
    by Chapman & Hall

    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


    Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger