Bayesian Methods in Pharmaceutical Research 1st Edition by Emmanuel Lesaffre,Gianluca Baio,Bruno Boulanger ISBN 113874848X 978-1138748484

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Product details:

ISBN 10: 113874848X 

ISBN 13: 978-1138748484

Author: Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger

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.

Table of contents:

I Introductory Part

• Chapter 1: Bayesian Background

• Chapter 2: FDA Regulatory Acceptance of Bayesian Statistics

• Chapter 3: Bayesian Tail Probabilities for Decision Making

II Clinical Development

• Chapter 4: Clinical Development in the Light of Bayesian Statistics

• Chapter 5: Prior Elicitation

• Chapter 6: Use of Historical Data

• Chapter 7: Dose Ranging Studies and Dose Determination

• Chapter 8: Bayesian Adaptive Designs in Drug Development

• Chapter 9: Bayesian Methods for Longitudinal Data with Missingness

• Chapter 10: Survival Analysis and Censored Data

• Chapter 11: Benefit of Bayesian Clustering of Longitudinal Data: Study of Cognitive Decline for Precision Medicine

• Chapter 12: Bayesian Frameworks for Rare Disease Clinical Development Programs

• Chapter 13: Bayesian Hierarchical Models for Data Extrapolation and Analysis in Pediatric Disease Clinical Trials

III Post-Marketing

• Chapter 14: Bayesian Methods for Meta-Analysis

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

• Chapter 16: Bayesian Modeling for Economic Evaluation Using “Real World Evidence”

• Chapter 17: Bayesian Benefit-Risk Evaluation in Pharmaceutical Research

IV Product Development and Manufacturing

• Chapter 18: Product Development and Manufacturing

• Chapter 19: Process Development and Validation

• Chapter 20: Analytical Method and Assay

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

• Chapter 22: Content Uniformity Testing

• Chapter 23: Bayesian Methods for In Vitro Dissolution Drug Testing and Similarity Comparisons

• Chapter 24: Bayesian Statistics for Manufacturing

V Additional Topics

• Chapter 25: Bayesian Statistical Methodology in the Medical Device Industry

• Chapter 26: Program and Portfolio Decision-Making

 

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Tags: Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger, Bayesian Methods in, Pharmaceutical Research