Bayesian Methods in Pharmaceutical Research: 1st Edition (Hardback) book cover

Bayesian Methods in Pharmaceutical Research

1st Edition

Edited by Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger

Chapman and Hall/CRC

552 pages | 111 B/W Illus.

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Hardback: 9781138748484
pub: 2020-02-17
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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.

Emmanuel Lesaffre is Professor of Biostatistics at KU Leuven, Belgium.

Gianluca Baio is Professor of Statistics and Health Economics at University College London, UK.

Bruno Boulanger is Chief Scientific Officer at PharmaLex, Belgium.

Table of Contents



List of abbreviations

I Introductory part

Chapter 1: Bayesian Background

Emmanuel Lesaffre and Gianluca Baio

1.1 Introduction

1.2 The frequentist approach to inference

1.3 Bayesian concepts

1.4 More than one parameter

1.5 Choosing the prior distribution

1.6 Determining the posterior distribution numerically

1.7 Hierarchical models and data augmentation

1.8 Model selection and model checking

1.9 Bayesian nonparametric methods

1.10 Bayesian software

1.11 Further reading

Chapter 2: FDA Regulatory Acceptance of Bayesian Statistics

Gregory Campbell

2.1 Introduction

2.2 Medical devices

2.3 Pharmaceutical products

2.4 Differences between devices and drugs

2.5 Some promising opportunities in pharmaceutical drugs

2.6 The future

2.7 Conclusion

Chapter 3: Bayesian Tail Probabilities for Decision Making

Leonhard Held

3.1 Introduction

3.2 Posterior tail probabilities

3.3 Predictive tail probabilities

3.4 Discussion

II Clinical development

Chapter 4: Clinical Development in the Light of Bayesian Statistics

David Ohlssen

4.1 Introduction

4.2 Introduction to drug development

4.3 Quantitative decision making in drug development

4.4 Bayesian thinking

4.5 Applications of Bayesian methods in drug development

4.6 Conclusion

Chapter 5: Prior Elicitation

Nicky Best, Nigel Dallow, and Timothy Montague

5.1 Introduction

5.2 Methods for prior elicitation

5.3 Examples

5.4 Impact and outlook

Chapter 6: Use of Historical Data

Beat Neuenschwander and Heinz Schmidli

6.1 Introduction

6.2 Identifying historical or co-data

6.3 An example: Guillain-Barre syndrome in children

6.4 Methods

6.5 Application: Non-inferiority trials

6.6 Discussion

6.7 Code

Chapter 7: Dose Ranging Studies and Dose Determination

Phil Woodward, Alun Bedding, and David Dejardin

7.1 Introduction

7.2 Dose-response studies

7.3 Dose escalation trials in oncology

7.4 Conclusions

Chapter 8: Bayesian Adaptive Designs in Drug Development

Gary L. Rosner

8.1 Introduction

8.2 Brief history of adaptive designs

8.3 What is an adaptive clinical trial?

8.4 Types of adaptation

8.5 Reasons we might consider adaptive designs

8.6 Example of an adaptive design

8.7 Adaptive enrichment designs

8.8 Some criticisms of adaptive designs

8.9 Summary

Chapter 9: Bayesian Methods for Longitudinal Data with Missingness

Michael J. Daniels and Dandan Xu

9.1 Introduction

9.2 Common frequentist approaches

9.3 Bayesian approaches

9.4 Ignorable and nonignorable missingness

9.5 Posterior inference

9.6 Model selection

9.7 Model checking and assessment

9.8 Practical example: Growth hormone trial

9.9 Wrap-up and open problems

Chapter 10: Survival Analysis and Censored Data

Linda D. Sharples and Nikolaos Demiris

10.1 Introduction

10.2 Review of survival analysis

10.3 Software

10.4 Applications

10.5 Reporting

10.6 Other comments

Chapter 11: Benefit of Bayesian Clustering of Longitudinal Data: Study of Cognitive

Decline for Precision Medicine

Anais Rouanet, Sylvia Richardson, and Brian Tom

11.1 Introduction

11.2 Motivating example

11.3 Nonparametric Bayesian models

11.4 Standard frequentist analysis: Latent class mixed models

11.5 Profile regression analysis

11.6 Conclusion

Chapter 12: Bayesian Frameworks for Rare Disease Clinical Development Programs

Freda Cooner, Forrest Williamson, and Bradley P. Carlin

12.1 Introduction

12.2 Natural history studies

12.3 Long-term safety evaluation with Real-World Data

12.4 Bayesian approaches in rare diseases

12.5 Case study

12.6 Conclusions and future directions

Chapter 13: Bayesian Hierarchical Models for Data Extrapolation and Analysis in

Pediatric Disease Clinical Trials

Cynthia Basu and Bradley P. Carlin

13.1 Introduction

13.2 Classical statistical approaches to data extrapolation

13.3 Current Bayesian approaches

13.4 Practical example

13.5 Outlook

III Post-marketing

Chapter 14: Bayesian Methods for Meta-Analysis

Nicky J Welton, Haley E Jones, and Sofia Dias

14.1 Introduction

14.2 Pairwise meta-analysis

14.3 Network meta-analysis

14.4 Bias modeling in pairwise and network meta-analysis

14.5 Using meta-analysis to inform study design

14.6 Further reading

Chapter 15: Economic Evaluation and Cost-E_ectiveness of Health Care Interventions

Nicky J Welton, Mark Strong, Christopher Jackson, and Gianluca Baio

15.1 Introduction

15.2 Economic evaluation: A Bayesian decision theoretic analysis

15.3 Trial-based economic evaluation

15.4 Model-based economic evaluation

15.5 Value of information

15.6 Conclusion / outlook

Chapter 16: Bayesian Modeling for Economic Evaluation Using "Real World Evidence"

Gianluca Baio

16.1 Introduction

16.2 Real World Evidence

16.3 Economic modeling and survival analysis

16.4 Case study: ICDs in cardiac arrhythmia

16.5 Conclusions and further developments

Chapter 17: Bayesian Bene_t-Risk Evaluation in Pharmaceutical Research

Carl Di Casoli, Yueqin Zhao, Yannis Jemiai, Pritibha Singh, and Maria Costa

17.1 Introduction

17.2 Classical approaches to quantitative bene_t-risk

17.3 Bayesian approaches to quantitative bene_t-risk

17.4 Outlook for Bayesian bene_t-risk

17.5 Discussion

IV Product development and manufacturing

Chapter 18: Product Development and Manufacturing

Bruno Boulanger and Timothy Mutsvari

18.1 Introduction

18.2 What is the question in manufacturing?

18.3 Bayesian statistics for comparability and analytical similarity

18.4 Bayesian approach to comparability and biosimilarity

18.5 Conclusions

Chapter 19: Process Development and Validation

John J. Peterson

19.1 Introduction

19.2 ICH Q8 design space

19.3 Assay robustness

19.4 Challenges for the Bayesian approach

Chapter 20: Analytical Method and Assay

Pierre Lebrun and Eric Rozet

20.1 Introduction

20.2 Analytical quality by design

20.3 Assay development

20.4 Analytical validation and transfer

20.5 Routine

20.6 Conclusion

Chapter 21: Bayesian Methods for the Design and Analysis of Stability Studies

Tonakpon Hermane Avohou, Pierre Lebrun, Eric Rozet, and Bruno Boulanger

21.1 Introduction

21.2 New perspectives on stability data analysis

21.3 Stability designs, models and assumptions

21.4 Overview of frequentist methods in stability data

21.5 Bayesian methods of analysis of stability data

21.6 Conclusions

Chapter 22: Content Uniformity Testing

Steven Novick and Bu_y Hudson-Curtis

22.1 Introduction

22.2 Classical procedures for testing content uniformity

22.3 Bayesian procedures for testing content uniformity and risk

22.4 Challenges for the Bayesian procedures

Chapter 23: Bayesian methods for in vitro dissolution drug testing and similarity


Linas Mockus and Dave LeBlond

23.1 Introduction

23.2 Current statistical practices in IV dissolution and their limitations

23.3 The value of adopting Bayesian paradigms

23.4 Applying Bayesian approaches: Two examples

23.5 Conclusions

Chapter 24: Bayesian Statistics for Manufacturing

Tara Scherder and Katherine Giacoletti

24.1 Introduction

24.2 Manufacturing situation 1: Revalidation/transfer

24.3 Manufacturing situation 2: Evaluating process capability

24.4 Manufacturing situation 3: Bayesian modeling of complex testing schemes

24.5 Discussion

V Additional topics

Chapter 25: Bayesian Statistical Methodology in the Medical Device Industry

Tarek Haddad

25.1 Introduction

25.2 Use of stochastic engineering models in the medical device design stage

25.3 Bayesian design and analysis of medical device trials

25.4 Challenges

Chapter 26: Program and Portfolio Decision-Making

Nitin Patel, Charles Liu, Masanori Ito, Yannis Jemiai, Suresh Ankolekar, and Yusuke


26.1 Introduction

26.2 Classical approaches

26.3 Current Bayesian approaches to program design

26.4 Program and portfolio-level Bayesian decision analysis

26.5 Research opportunities


About the Editors

Emmanuel Lesaffre

Emmanuel Lesaffre studied mathematics at the University of Antwerp and received his PhD in statistics at the University of Leuven, Belgium. He is full professor at L-Biostat, KU Leuven, and part-time professor at University of Hasselt. He had a joint position at Erasmus University in Rotterdam, the Netherlands from 2007 to 2014.

His statistical research is rooted in medical research questions. He has worked in a great variety of medical research areas, but especially in oral health, cardiology, nursing research, ophthalmology and oncology. He also contributed on various statistical topics, i.e. discriminant analysis, hierarchical models, model diagnostics, interval-censored data, misclassification issues, variable selection, various clinical trial topics and diagnostic tests both under the frequentist and Bayesian paradigm. He has taught introductory and advanced courses to medical and statistical researchers. In the last two decades, his research focused on Bayesian techniques resulting in a textbook and courses taught at several universities and governmental organizations. Recently, he co-authored a textbook on interval censoring. In total he (co)-authored nine books and more than 600 papers. He has served as statistical consultant on a great variety of clinical trials in various ways, e.g. as a steering committee and data-monitoring committee member.

He is the founding chair of the Statistical Modelling Society (2002) and was ISCB president (2006-2008). Further, he is ASA and ISI fellow and honorary member of the Society for Clinical Biostatistics and of the Statistical Modelling Society. He has been involved in the organisation of the Bayes 20XX conference since 2013.

Gianluca Baio

Gianluca Baio is a Professor of Statistics and Health Economics in the Department of Statistical Science at University College London. He graduated in Statistics and Economics from the University of Florence (Italy). He then completed a PhD programme in Applied Statistics again at the University of Florence, after a period at the Program on the Pharmaceutical Industry at the MIT Sloan School of Management, Cambridge (USA). I then worked as a Research Fellow and then Lecturer in the Department of Statistical Sciences at University College London (UK). His main interests are in Bayesian statistical modelling for cost effectiveness analysis and decision-making problems in the health systems, hierarchical/multilevel models and causal inference using the decision-theoretic approach. He also leads the Statistics for Health Economic Evaluation research group within the department of Statistical Science, whose activity revolves around the development and application of Bayesian statistical methodology for health economic evaluation, e.g. cost-effectiveness or cost-utility analysis. He also collaborates with the UK National Institute for Health and Care Excellence (NICE) as a Scientific Advisor on Health Technology Appraisal projects and has served as Secretary (2014-2016) and then Programme Chair (2016-2018) in the Section on Biostatistics and Pharmaceutical Statistics of the International Society for Bayesian Analysis. He has been involved in the organisation of the Bayes 20XX conference since 2013.

Bruno Boulanger

Bruno Boulanger, Ph.D.

Organization: PharmaLex Belgium

Dr Bruno Boulanger,

Chief Scientific Officer, PharmaLex Belgium Belgium

Lecturer, School of Pharmacy, Université de Liège, Belgium

After a post-doctorate at the Université Catholique de Louvain (Belgium) and the University of Minnesota (USA) in Statistics applied to simulation of clinical trials, Bruno joined Eli Lilly in Belgium in 1992. Bruno holds various positions in Europe and in the USA where he gathered experience in several areas of pharmaceutical industry including discovery, toxicology, CMC and early clinical phases. Bruno joined UCB Pharma in 2007 as Director of Exploratory Statistics, contributing the implementation of Model-Based Drug Development strategy and applied Bayesian statistics. Bruno is also since 2000 Lecturer at the Université of Liège, in the School of Pharmacy, teaching Design of Experiments and Statistics. Bruno organizes and contributes since 1998 to Non-Clinical Statistics Conference in Europe and setup in 2010 the Applied Bayesian Biostatistics conference. Bruno is also a USP Expert, member of the Committee of Experts in Statistics since 2010. Bruno has authored or co-authored more than 100 publications in applied statistics.

About the Series

Chapman & Hall/CRC Biostatistics Series

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Subject Categories

BISAC Subject Codes/Headings:
MATHEMATICS / Probability & Statistics / General
MEDICAL / Pharmacology
MEDICAL / Biostatistics