Bayesian methods have emerged as the driving force for methodological development in drug development. This edited book provides broad coverage of Bayesian methods in pharmaceutical research. The book includes contributions from some of the leading researchers in the field, and has been edited to ensure consistency in level and style.

Emmanuel Lesaffre, Gianluca Baio, Bruno Boulanger

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