Bayesian methods in pharmaceutical research

by Baio, Gianluca Boulanger, Bruno Lesaffre, Emmanuel

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Title Statements

Full title :Bayesian methods in pharmaceutical research / edited by Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger.

Summary

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.

Edition Statements

Publication Date:2020.

Publication Statements

Publisher(s):CRC Press
Place of Publication:Boca Raton, FL
Language:eng
Publication Date:2020.

The Physical Object

Description:1 online resource (xxx, 516 pages) : : illustrations

Edition Identifiers

ISBN:9781315180212, 1315180219, 9781351718677, 1351718673, 9781351718660, 1351718665
oclc:on1152525270
LC Classification:RS57
Dewey Decimal:615.19001519542

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 inPedi…

Subjects

Bayes Theorem Bayesian statistical decision theory Clinical trials -- Statistical methods Clinical Trials as Topic -- methods Decision making -- Mathematical models Decision Support Techniques Études cliniques -- Méthodes statistiqu… Pharmacy -- Research Prise de décision -- Modèles mathématiq… Technology Assessment, Biomedical Théorème de Bayes Théorie de la décision bayésienne