Real-World Evidence in Drug Development and Evaluation
1st Edition
Published on 11. January 2021
190 pages
978-0-429-67681-9 (ISBN)
Description
Real-world evidence (RWE) has been at the forefront of pharmaceutical innovations. It plays an important role in transforming drug development from a process aimed at meeting regulatory expectations to an operating model that leverages data from disparate sources to aid business, regulatory, and healthcare decision making. Despite its many benefits, there is no single book systematically covering the latest development in the field.
Written specifically for pharmaceutical practitioners, Real-World Evidence in Drug Development and Evaluation, presents a wide range of RWE applications throughout the lifecycle of drug product development. With contributions from experienced researchers in the pharmaceutical industry, the book discusses at length RWE opportunities, challenges, and solutions.
Features
Provides the first book and a single source of information on RWE in drug development
Covers a broad array of topics on outcomes- and value-based RWE assessments
Demonstrates proper Bayesian application and causal inference for real-world data (RWD)
Presents real-world use cases to illustrate the use of advanced analytics and statistical methods to generate insights
Offers a balanced discussion of practical RWE issues at hand and technical solutions suitable for practitioners with limited data science expertise
Written specifically for pharmaceutical practitioners, Real-World Evidence in Drug Development and Evaluation, presents a wide range of RWE applications throughout the lifecycle of drug product development. With contributions from experienced researchers in the pharmaceutical industry, the book discusses at length RWE opportunities, challenges, and solutions.
Features
Provides the first book and a single source of information on RWE in drug development
Covers a broad array of topics on outcomes- and value-based RWE assessments
Demonstrates proper Bayesian application and causal inference for real-world data (RWD)
Presents real-world use cases to illustrate the use of advanced analytics and statistical methods to generate insights
Offers a balanced discussion of practical RWE issues at hand and technical solutions suitable for practitioners with limited data science expertise
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
20 Tables, black and white; 30 Illustrations, black and white
ISBN-13
978-0-429-67681-9 (9780429676819)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Harry Yang | Binbing Yu
Real-World Evidence in Drug Development and Evaluation
Book
08/2022
1st Edition
Chapman & Hall/CRC
€66.00
Shipment within 15-20 days
Harry Yang | Binbing Yu
Real-World Evidence in Drug Development and Evaluation
Book
03/2021
1st Edition
Chapman & Hall/CRC
€161.21
Shipment within 15-20 days
Persons
Harry Yang, Ph.D., is Vice President and Head of Biometrics at Fate Therapeutics. He has 25 years of experience across all aspects of drug research and development, from early target discovery, through pre-clinical, clinical, and CMC programs to regulatory approval and post-approval lifecycle management. He has published 7 statistical books, 15 book chapters, and over 90 peer-reviewed papers on diverse scientific and statistical subjects. He is a frequent invited speaker at national and international conferences. He also developed statistical courses and conducted training at the FDA and USP.
Binbing Yu, Ph.D., is Associate Director in the Oncology Statistical Innovation group at AstraZeneca. He serves as the statistical expert across the whole spectrum of drug R&D, including drug discovery, clinical trials, operation and manufacturing, clinical pharmacology, oncology medical affairs and post-marketing surveillance. He obtained his PhD in Statistics from the George Washington University. His primary research interests are clinical trial design and analysis, cancer epidemiology, causal inference in observation studies, PKPD modeling and Bayesian analysis.
Binbing Yu, Ph.D., is Associate Director in the Oncology Statistical Innovation group at AstraZeneca. He serves as the statistical expert across the whole spectrum of drug R&D, including drug discovery, clinical trials, operation and manufacturing, clinical pharmacology, oncology medical affairs and post-marketing surveillance. He obtained his PhD in Statistics from the George Washington University. His primary research interests are clinical trial design and analysis, cancer epidemiology, causal inference in observation studies, PKPD modeling and Bayesian analysis.
Content
1 Using Real-world Evidence to Transform Drug Development: Opportunities and Challenges. 2. Evidence derived from real world data: utility, constraints and cautions. 3. Real-World Evidence from Population-Based Cancer Registry Data. 4. External Control using RWE and Historical Data in Clinical Development. 5. Bayesian method for assessing drug safety using real-world evidence. 6. Real-World Evidence for Coverage and Payment Decisions. 7. Causal Inference for Observational Studies/Real-World Data. 8. Introduction to Artificial Intelligence and Deep Learning with a Case Study in Analyzing Electronic Health Records for Drug Development.
