This book concerns use of real world data (RWD) and real world evidence (RWE) to aid drug development across product cycle. RWD are healthcare data that are collected outside the constraints of conventual controlled randomized trials (CRTs); whereas RWE is the knowledge derived from aggregation and analysis of RWD.
This book concerns use of real world data (RWD) and real world evidence (RWE) to aid drug development across product cycle. RWD are healthcare data that are collected outside the constraints of conventual controlled randomized trials (CRTs); whereas RWE is the knowledge derived from aggregation and analysis of RWD.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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.
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