Bayesian Methods in Pharmaceutical Research

Bayesian Methods in Pharmaceutical Research, edited by Emmanuel Lesaffre, Bruno Boulanger and Gianluca Baio and published by CRC, collates contributions by leading researchers in the various aspects of Bayesian modelling within the pharmaceutical industry.

The book covers all main areas of pharmaceutical development, from pre-clinical to post-marketing studies, highlighting the Bayesian contributions and advantages and will be out in early 2020. More to come in here too!

Note

You can get a 30% discount on retail price using the code ASA18.

Table of content

Part 1: Introduction

• Chapter 1: Bayesian Background (Emmanuel Lesaffre and Gianluca Baio)
  
• Chapter 2: FDA Regulatory Acceptance of Bayesian Statistics (Gregory Campbell)
  
• Chapter 3: Bayesian Tail Probabilities for Decision Making (Leonhard Held)

Part 2: Clinical development

• Chapter 4: Clinical Development in the Light of Bayesian Statistics (David Ohlssen)
  
• Chapter 5: Prior Elicitation (Nicky Best, Nigel Dallow, and Timothy Montague)
  
• Chapter 6: Use of Historical Data (Beat Neuenschwander and Heinz Schmidli)
  
• Chapter 7: Dose Ranging Studies and Dose Determination (Phil Woodward, Alun Bedding, and David Dejardin)
  
• Chapter 8: Bayesian Adaptive Designs in Drug Development (Gary L. Rosner)
  
• Chapter 9: Bayesian Methods for Longitudinal Data with Missingness (Michael J. Daniels and Dandan Xu)
  
• Chapter 10: Survival Analysis and Censored Data (Linda D. Sharples and Nikolaos Demiris)
  
• Chapter 11: Benefit of Bayesian Clustering of Longitudinal Data: Study of Cognitive Decline for Precision Medicine (Anais Rouanet, Sylvia Richardson, and Brian Tom)
  
• Chapter 12: Bayesian Frameworks for Rare Disease Clinical Development Programs (Freda Cooner, Forrest Williamson, and Bradley P. Carlin)
  
• Chapter 13: Bayesian Hierarchical Models for Data Extrapolation and Analysis in Pediatric Disease Clinical Trials (Cynthia Basu and Bradley P. Carlin)

Part 3: Post-marketing

• Chapter 14: Bayesian Methods for Meta-Analysis (Nicky J Welton, Haley E Jones, and Sofia Dias)
  
• Chapter 15: Economic Evaluation and Cost-Effectiveness of Health Care Interventions (Nicky J Welton, Mark Strong, Christopher Jackson, and Gianluca Baio)
  
• Chapter 16: Bayesian Modeling for Economic Evaluation Using “Real World Evidence” (Gianluca Baio)
  
• Chapter 17: Bayesian Benefit-Risk Evaluation in Pharmaceutical Research (Carl Di Casoli, Yueqin Zhao, Yannis Jemiai, Pritibha Singh, and Maria Costa)

Part 4: Product development and manufacturing

• Chapter 18: Product Development and Manufacturing (Bruno Boulanger and Timothy Mutsvari)
  
• Chapter 19: Process Development and Validation (John J. Peterson)
  
• Chapter 20: Analytical Method and Assay (Pierre Lebrun and Eric Rozet)
  
• Chapter 21: Bayesian Methods for the Design and Analysis of Stability Studies (Tonakpon Hermane Avohou, Pierre Lebrun, Eric Rozet, and Bruno Boulanger)
  
• Chapter 22: Content Uniformity Testing (Steven Novick and Buffy Hudson-Curtis)
  
• Chapter 23: Bayesian methods for in vitro dissolution drug testing and similarity comparisons (Linas Mockus and Dave LeBlond)
  
• Chapter 24: Bayesian Statistics for Manufacturing (Tara Scherder and Katherine Giacoletti)

Part 5: Additional topics

• Chapter 25: Bayesian Statistical Methodology in the Medical Device Industry (Tarek Haddad)
  
• Chapter 26: Program and Portfolio Decision-Making (Nitin Patel, Charles Liu, Masanori Ito, Yannis Jemiai, Suresh Ankolekar, and Yusuke Yamaguchi)