Bayesian Applications in Pharmaceutical Development Chapman & Hall/CRC Biostatistics Series
Coordonnateurs : Lakshminarayanan Mani, Natanegara Fanni
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 and statistical consultation at Eli Lilly. Dr. Natanegara is the chair of the cross industry-regulatory-academic DIA BSWG to ensure that Bayesian methods are appropriately utilized for design and analysis throughout the drug-development process.
Introduction. Building prior. Discover/preclinical phase. Clinical phase. Product development. Regulatory overview of Bayesian application. Computational tools. Special 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 Ph.D. 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 and statistical consultation at Eli Lilly. Dr. Natanegara is the chair of the cross industry-regulatory-academic DIA BSWG to ensure that Bayesian methods are appropriately utilized for design and analysis throughout the drug-development process.
Date de parution : 09-2021
15.6x23.4 cm
Date de parution : 10-2019
15.6x23.4 cm
Thèmes de Bayesian Applications in Pharmaceutical Development :
Mots-clés :
MMRM; MMRM Analysis; drug development; Bayesian Predictive Probabilities; quantitative evaluation; PoC Study; prior distribution; Posterior Distribution; health care; Data Monitoring Committees; decision making; Interim Analysis; clinical trials; Posterior Predictive Distribution; Bayesian applications; Bayesian Sample Size Determination; drug development process; Non-informative Prior; innovative trial designs; PASI75 Response; pharmaceutical development; Prior Distributions; Bayesian Adaptive Design; Target DLT Rate; Unchallenged Group; Adaptive Randomization; Regulatory Reviewers; Prior Information; Adaptive Design; Drug Combination Trials; Effective Sample Size; Study Ii; Missing Data; SMA Type; DLT Rate