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.
About the Author:

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.