TUM Library

Chapter 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 CognitiveDecline 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 inPediatric 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-E_ectiveness 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 Bene_t-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 Bu_y Hudson-CurtisChapter 23: Bayesian methods for in vitro dissolution drug testing and similaritycomparisons 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 YusukeYamaguchi

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.

Includes index.

OCLC-licensed vendor bibliographic record.