图书简介
The cost for bringing new medicine from discovery to market has nearly doubled in the last decade and has now reached $2.6 billion. There is an urgent need to make drug development less time-consuming and less costly. Innovative trial designs/ analyses such as the Bayesian approach are essential to meet this need. This book will be the first to provide comprehensive coverage of Bayesian applications across the span of drug development, from discovery, to clinical trial, to manufacturing with practical examples. This book will have a wide appeal to statisticians, scientists, and physicians working in drug development who are motivated to accelerate and streamline the drug development process, as well as students who aspire to work in this field. The advantages of this book are: Provides motivating, worked, practical case examples with easy to grasp models, technical details, and computational codes to run the analyses Balances practical examples with best practices on trial simulation and reporting, as well as regulatory perspectives Chapters written by authors who are individual contributors in their respective topics Dr. Mani Lakshminarayanan is a researcher and statistical consultant with more than 30 years of experience in the pharmaceutical industry. He has published over 50 articles, technical reports, and book chapters besides serving as a referee for several journals. He has a PhD in Statistics from Southern Methodist University, Dallas, Texas and is a Fellow of the American Statistical Association. Dr. Fanni Natanegara has over 15 years of pharmaceutical experience and is currently Principal Research Scientist and Group Leader for the Early Phase Neuroscience Statistics team at Eli Lilly and Company. She played a key role in the Advanced Analytics team to provide Bayesian education a
Preface 1. Introduction 2. Incorporation of Historical Control Data in Analysis of Clinical Trials 3. Practical considerations for building priors for confirmatory studies 4. The Practice of Prior Elicitation 5. Bayesian examples in preclinical in-vivo research 6. Planning a model-based Bayesian dose response study 7. Novel Designs for Early Phase Drug Combination Trials 8. Executing and Reporting Clinical Trial Simulations: Practical Recommendations for Best Practices 9. Reporting of Bayesian Analyses in Clinical Research: Some Recommendations 10. Handling missing data in clinical trials with Bayesian and Frequentist Approaches 11. Bayesian Applications in Pharmaceutical Development 12. Simulation for Bayesian Adaptive Designs - Step-by-Step Guide for Developing the Necessary R Code 13. Power Priors for Sample Size Determination in the Process Validation Life-Cycle 14. Bayesian Approaches in the Regulation of Medical Products 15. Computational tools 16. Software for Bayesian Computation - An Overview of Some Currently Available Tools 17. Considerations and Bayesian Applications in Pharmaceutical Development for Rare Diseases 18. Extrapolation Process in Pediatric Drug Development and Corresponding Bayesian Implementation for Validating Clinical Efficacy 19. BM- Appendix: A Brief Guide to Bayesian Model Checking
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