Global simultaneous development is becoming more necessary as the cost of developing medical products continues to grow. The strategy of using multiregional clinical trials (MRCTs) has become the preferred method for developing new medicines. Implementing the same protocol to include subjects from many geographical regions around the world, MRCTs can speed up the patient enrolment, thus resulting in quicker drug development and obtaining faster approval of the drug globally. After the publication of the editors' first volume on this topic, there have been new developments on MRCTs. The…mehr
Global simultaneous development is becoming more necessary as the cost of developing medical products continues to grow. The strategy of using multiregional clinical trials (MRCTs) has become the preferred method for developing new medicines. Implementing the same protocol to include subjects from many geographical regions around the world, MRCTs can speed up the patient enrolment, thus resulting in quicker drug development and obtaining faster approval of the drug globally. After the publication of the editors' first volume on this topic, there have been new developments on MRCTs. The International Council for Harmonisation (ICH) issued ICH E17, a guideline document on MRCTs, in November 2017, laying out principles on MRCTs. Beyond E17, new methodologies have been developed as well. Simultaneous Global New Drug Development: Multi-Regional Clinical Trials after ICH E17 collects chapters providing interpretations of principles in ICH E17 and new ideas of implementing MRCTs. Authors are from different regions, and from academia and industry. In addition, in contrast to the first book, new perspectives are brought to MRCT from regulatory agencies. This book will be of particular interest to biostatisticians working in late stage clinical development of medical products. It will also be especially helpful for statisticians in regulatory agencies, and medical research institutes. This book is comprehensive across the MRCT topic spectrum, including Issues regarding ICH E17 Implementation MRCT Design and Analysis Methodologies Perspectives from authorities in regulatory agencies, as well as statisticians practicing in the medical product industry Many examples of real-life applications based on actual MRCTs. ¿Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. Gang Li is Senior Director, Real World Evidence (RWE) and Medical Value, the Neurology Business Group of Eisai Inc. He received his PhD in Mathematical Statistics from the State University of New York at Binghamton. He co-authored over 60 publications on statistical methodologies, and psychiatry, obesity and diabetes. He served as the Executive Director of the International Statistical Association (2017 - 2019). Dr. Li is a Fellow of the American Statistical Association. Dr. Hui Quan is currently the global head of the Statistical Research group of the Biostatistics and Programming Department of Sanofi. He received his PhD degree in statistics from Columbia University in 1990. He has 30 years of pharmaceutical industry experience in many therapeutic areas. He has published 108 papers including 84 statistical papers. He is a co-author/co-editor of three books. He has served as an associate editor for two journals. He is a fellow of the American Statistical Association. Dr. William (Bill) Wang is executive director in the department of biostatistics and research decision sciences (BARDS), Merck Research Laboratories. He has 25+ years of experience in clinical trial design, biostatistics, data management, and regulatory filings across multiple TAs including vaccine and oncology. From 2007 to 2014, Bill had led the design, build-up and day-to-day oversight of global biometrics operation in China. From 2014 to present, Bill has led the establishment of the clinical safety statistics group to support the clinical safety model. He is currently responsible for managing the global clinical safety statistics operation and the late develop statistics in the Asia Pacific region. Through his 20+ years tenure at Merck & Co, Inc, Bill has led the study design, execution, filing and approval by multi-regional regulatory agencies. Bill has served as the deputy topics-leader for the ICH E17 working group. The ICH E17 is an ICH efficacy guidance for designing multi-regional clinical trials in global simultaneous drug development and regulatory approval. Bill has also co-founded and co-chaired an American Statistical Association (ASA) Interdisciplinary Safety Working Group. Dr Bill Wang received the DIA Inspire Award in 2017 and was elected as a Fellow of American Statistical Association in 2018. Dr Bruce Binkowitz earned his PhD from what is now named the Rutgers School of Public Health. He is the Vice President of Biometrics at Shionogi, Inc. in Florham Park, New Jersey. Bruce has more than 30 years of pharmaceutical industry experience across many therapeutic areas as well as from early phase through phase IV clinical trials. He has experience in study design, conduct, analysis and interpretation of results for clinical trials, as well as many interactions with health authorities worldwide. Dr. Binkowitz is active in the Statistical Community, having served many leadership roles including a 3 year term on the ENAR Regional Board of Advisors; as part of the DIA Statistics Core Community including a 3 year term as the industry representative responsible for organizing the Statistics Track for the DIA Annual Meeting; and has served the statistical community as member of the American Statistical Association including as a 5-year member of the steering committee for the ASA Biopharmaceutical Section FDA/Industry Statistics Workshop, culminating as the 2013 Workshop Industry Chair. Most recently Bruce was honored to be elected as the 2020 chair of the ASA Biopharmaceutical Section. Bruce is currently in his second term as co-leader fo the cross-industry MutliRegional Clinical Trial Consistency Working Group. Bruce has taught numerous short courses on MultiRegional Clinical Trials, has been the lead or co-author on more than 40 published papers across the Statistical and Medical Literature as well as lead author or co-author on more than 45 conference and workshop presentations. Dr. Binkowitz was elected a fellow of the American Statistical Association in 2015. Dr. Yonghua (Joshua) Chen received his PhD in Statistics from the University of Wisconsin-Madison, and Master and Bachelor degrees in Probability and Statistics from Peking University. He is currently the Head of Global Biostatistical Sciences at Sanofi Pasteur. Before joining Pasteur, Josh worked on clinical development of small molecules, biologics and vaccines at Merck Research Labs. His experience spans many therapeutic areas with major focus on human vaccines and antiviral drugs. He has extensive experience in study design, conduct and reporting of international clinical trials from proof-of-concept through regulatory approvals and life cycle management. His primary research interest is clinical trial designs, including group sequential methods, adaptive designs and multiregional clinical trials (MRCTs). He was a co-lead of the across-industry MRCT Consistency Working Group under PhRMA (2008-2011) and Society for Clinical Trials (2012-2014). Dr. Chen is a Fellow of the American Statistical Association.
Inhaltsangabe
1. General Introduction of E17. 2. New Principles in Global development using ICH E 17 guideline. 3. Consideration of "Ethnic Factors" as Defined in ICH E5 for Drug Development and Application in ICH E17 Guidelines for Conduct of Multiregional Clinical Trials (MRCT). 4. Pooling Strategy. 5. Overall sample size and allocation to regions. 6. Consistency Evaluation in MRCTs. 7. Implementation of the ICH E17 guideline in Japan. 8. ICH E17 and MRCT: Implementation and Regulatory Perspective in China. 9. Lessons Learned from Actual Multi-regional Clinical Trials with Signals of Treatment Effect Heterogeneity. 10. Leveraging Foreign Clinical Data Extrapolation to Accelerate Global Drug Development: A Case Study in Hematological Oncology. 11. Analysis Models for Multi-regional Clinical Trials. 12. Utilization of Robust Estimates of Treatment Effects via Semi-Parametric Models in MRCT. 13. Hierarchical Linear Models for Multi-Regional Clinical Trials. 14. Local Treatment Effect Estimation by Borrowing Information from Similar Regions in Multi-Regional Clinical Trials. 15. Global Drug Development: Multi-Regional Clinical Trials (MRCTs) - China's Pursuit. 16. Statistical considerations in MRCT design and analysis. 17. Multiple Endpoints in Multiregional Clinical Trials. 18. Multiregional Clinical Trials with Heterogeneous Regional and Treatment Effects. 19. Regional sample size calculation in multiregional equivalence and non-inferiority trials
1. General Introduction of E17. 2. New Principles in Global development using ICH E 17 guideline. 3. Consideration of "Ethnic Factors" as Defined in ICH E5 for Drug Development and Application in ICH E17 Guidelines for Conduct of Multiregional Clinical Trials (MRCT). 4. Pooling Strategy. 5. Overall sample size and allocation to regions. 6. Consistency Evaluation in MRCTs. 7. Implementation of the ICH E17 guideline in Japan. 8. ICH E17 and MRCT: Implementation and Regulatory Perspective in China. 9. Lessons Learned from Actual Multi-regional Clinical Trials with Signals of Treatment Effect Heterogeneity. 10. Leveraging Foreign Clinical Data Extrapolation to Accelerate Global Drug Development: A Case Study in Hematological Oncology. 11. Analysis Models for Multi-regional Clinical Trials. 12. Utilization of Robust Estimates of Treatment Effects via Semi-Parametric Models in MRCT. 13. Hierarchical Linear Models for Multi-Regional Clinical Trials. 14. Local Treatment Effect Estimation by Borrowing Information from Similar Regions in Multi-Regional Clinical Trials. 15. Global Drug Development: Multi-Regional Clinical Trials (MRCTs) - China's Pursuit. 16. Statistical considerations in MRCT design and analysis. 17. Multiple Endpoints in Multiregional Clinical Trials. 18. Multiregional Clinical Trials with Heterogeneous Regional and Treatment Effects. 19. Regional sample size calculation in multiregional equivalence and non-inferiority trials
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