This book discusses a multidisciplinary and multi-stakeholder collaborative approach to small population clinical trials. It provides and illustrates a systematic framework that depends upon a collaborative infrastructure between the patient community, patient advocacy groups, researchers and investigators, pharmaceutical companies, global regulatory agencies, and customers. The book also highlights the considerations of natural history and patient registry, diagnostic criteria determination, identification of clinically meaningful endpoints, and global regulatory guidance. The authors detail the nuances of clinical trial design as specific to small population disease, drawing from real examples.
表中的内容
1. Introduction.- 2. The Case Study of NCI-COG Pediatric Match Trials.- 3. The Case Study of Non-oncology (Lonafarnib).- 4. Natural History and Patient Registry.- 5. Diagnostic Criteria and Endpoints.- 6. Enrichment Designs.- 7. Pediatric Extrapolation.- 8. Cross-over Designs.- 9. Adaptive Designs.- 10. Master Protocols.- 11. Other Designs.- 12. Modeling and Simulations.- 13. Frequentist Approaches.- 14. Bayesian Approaches.- 15. Operation and Other Considerations.- 16. Case Studies.
关于作者
Jingjing Ye is a Fellow of American Statistical Association and is currently an executive director and global head of Data Science and Digital Innovations (DSDI), with Global Statistics and Data Sciences (GSDS) in Bei Gene. She leads a global team with diverse background supporting business functions in R&D and outside R&D for their data, analytics and technology support. She has over 17 years’ experience in pharmaceutical industry and US FDA, with focus in cancer drug discovery and development. Her statistical and regulatory experience expands full spectrum on patients’ treatment journey from diagnosis, treatment, to living with the condition. Her main research interest is the innovative trial designs and analyses related to small populations, e.g., pediatric and rare diseases. She received her Ph D degree in statistics from University of California, Davis and BS in applied mathematics from Peking University.
Lei Nie graduated from the University of Illinois at Chicago with a Ph.D. in Statistics in 2002. Dr. Nie has worked for the U.S. Food and Drug Administration, Georgetown University and University of Maryland Baltimore Country. He is an elected fellow of the American Statistical Association. He loves multidisciplinary collaboration and authored/authored more than 100 peer reviewed journal publications.
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