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 computingBayesian biostatistics for clinical innovative designsAdding value with Real World EvidenceOpportunities for rare, orphan diseases, and pediatric developmentApplied Bayesian biostatistics in manufacturingDecision 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 PartChapter 1: Bayesian Background Emmanuel Lesaffre and Gianluca BaioChapter 2: FDA Regulatory Acceptance of Bayesian StatisticsGregory CampbellChapter 3: Bayesian Tail Probabilities for Decision Making Leonhard HeldII Clinical Development Chapter 4: Clinical Development in the Light of Bayesian Statistics David OhlssenChapter 5: Prior ElicitationNicky Best, Nigel Dallow, and Timothy MontagueChapter 6: Use of Historical Data Beat Neuenschwander and Heinz SchmidliChapter 7: Dose Ranging Studies and Dose Determination Phil Woodward, Alun Bedding, and David DejardinChapter 8: Bayesian Adaptive Designs in Drug Development Gary L. RosnerChapter 9: Bayesian Methods for Longitudinal Data with MissingnessMichael J. Daniels and Dandan XuChapter 10: Survival Analysis and Censored Data Linda D. Sharples and Nikolaos DemirisChapter 11: Benefit of Bayesian Clustering of Longitudinal Data: Study of Cognitive Decline for Precision Medicine Anais Rouanet, Sylvia Richardson, and Brian TomChapter 12: Bayesian Frameworks for Rare Disease Clinical Development Programs Freda Cooner, Forrest Williamson, and Bradley P. CarlinChapter 13: Bayesian Hierarchical Models for Data Extrapolation and Analysis in Pediatric Disease Clinical Trials Cynthia Basu and Bradley P. CarlinIII Post-Marketing Chapter 14: Bayesian Methods for Meta-AnalysisNicky J Welton, Haley E Jones, and Sofia DiasChapter 15: Economic Evaluation and Cost-Effectiveness of Health Care InterventionsNicky J Welton, Mark Strong, Christopher Jackson, and Gianluca BaioChapter 16: Bayesian Modeling for Economic Evaluation Using "Real World Evidence"Gianluca BaioChapter 17: Bayesian Benefit-Risk Evaluation in Pharmaceutical Research Carl Di Casoli, Yueqin Zhao, Yannis Jemiai, Pritibha Singh, and Maria CostaIV Product Development and Manufacturing Chapter 18: Product Development and Manufacturing Bruno Boulanger and Timothy MutsvariChapter 19: Process Development and Validation John J. PetersonChapter 20: Analytical Method and Assay Pierre Lebrun and Eric RozetChapter 21: Bayesian Methods for the Design and Analysis of Stability Studies Tonakpon Hermane Avohou, Pierre Lebrun, Eric Rozet, and Bruno BoulangerChapter 22: Content Uniformity Testing Steven Novick and Buffy Hudson-CurtisChapter 23: Bayesian methods for in vitro dissolution drug testing and similarity comparisons Linas Mockus and Dave LeBlondChapter 24: Bayesian Statistics for Manufacturing Tara Scherder and Katherine GiacolettiV Additional Topics Chapter 25: Bayesian Statistical Methodology in the Medical Device Industry Tarek HaddadChapter 26: Program and Portfolio Decision-Making Nitin Patel, Charles Liu, Masanori Ito, Yannis Jemiai, Suresh Ankolekar, and Yusuke Yamaguchi

Emmanuel LesaffreEmmanuel 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 BaioGianluca 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 BoulangerBruno Boulanger, Ph.D.Organization: PharmaLex Belgium Dr Bruno Boulanger, Chief Scientific Officer, PharmaLex Belgium BelgiumLecturer, School of Pharmacy, Université de Liège, BelgiumAfter 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.