TITLE: Application of Data Borrowing in Clinical Trials and Regulatory Submissions
SPEAKERS: Jerry Li, Vlad Son and Ivan F Chan, BMS
MODERATOR:Alfred H Balch
Abstract:
Data borrowing can bring significant benefits to expedite drug development bringing life-saving drugs to patients. Specifically, data borrowing can overcome challenges when patients are difficult to enroll, reduce the size/duration/risk of a new trial ensuring adequate power, have great operational and cost saving benefits, and boost the power and improve the efficiency of analysis for a trial with a limited sample size.
This tutorial will introduce the approaches of both frequentist and Bayesian borrowing. Frequent methods including propensity-score matching or weighting will be covered. Various Bayesian methods, including the power prior, commensurate prior, meta-analytic predictive (MAP) prior, robust MAP prior (RMAP) and self-adaptive mixture (SAM) prior, will be elaborated. Case studies and the regulatory landscape will also be introduced.
Instructors’ Biography:
Dr. Jerry Li is a Director and TA Lead in Oncological Biostatistics at Global Biostatistics and Data Sciences (GBDS), BMS. Jerry leads statistical support for the clinical development of multiple assets including BMS/BioNTech strategically partnered anti-PD1 and VEGF program for clinical trials design including phase2/3 seamless design, interactions with worldwide health authorities, and life cycle management of the assets.
Jerry established and co-lead the Dynamic Borrowing Working Group at BMS and co-authored the BMS internal guidance on this topic. Jerry also co-founded and co-chaired the ASA BIOP Dynamic Borrowing Scientific Working Group consisting of over two dozen members from health authority, academia and biopharmaceutical industry.
Prior to BMS, Jerry was at Merck and Daiichi Sankyo following working at the FDA. He has held positions with increasing responsibilities in multiple therapeutic areas including oncology, neurosciences, immunology, and infectious disease and demonstrated a track record of successful regulatory approvals. In addition to dynamic borrowing, Jerry is also interested in dose optimization, phase 2/3 seamless design, statistical modeling of disease-modifying treatment effect, and properties of log-rank test following covariate-adaptive randomization in oncology trials. Jerry received his Ph.D. in statistics from the University of Maryland, College Park, and also holds an advanced degree in biomedical sciences.
Dr. Vladimir (Vlad) Son is currently a Director and Ozanimod Lead in Immunology withinGlobal Biostatistics and Data Sciences (GBDS) at BMS. Prior to joining BMS, Vlad worked at Regeneron, contributing to multiple therapeutic areas, including ophthalmology and cardiovascular. He earned his Ph.D. in Statistics from Bowling Green State University.
Vlad serves as co-lead of the Regulatory Subteam within the Dynamic Borrowing Working Group. He is also the GBDS representative on the Policy Evaluation And Regulatory Landscape (PEARL) Council at BMS, and is an active member of the BMS Estimand Working Group.
Ivan S.F. Chan has extensive experience in the pharmaceutical industry. He is VP and Head of Oncology Biostatistics, Global Biometrics & Data Sciences at Bristol Myers Squibb, leading the global statistical support for oncology development. Prior to joining BMS, Ivan was VP and Head of Statistical Sciences at AbbVie, and he previously worked at Merck Research Laboratories where he led the global statistical support for vaccines. Ivan received his Ph.D. in Biostatistics from the University of Minnesota. He is an elected Fellow of the American Statistical Association (ASA) and an elected Fellow of the Society for Clinical Trials (SCT). He currently serves as Co-Chair of Deming Conference on Applied Statistics and Executive Director of the International Society for Biopharmaceutical Statistics. Ivan has previously served as the President of the International Chinese Statistical Association and the Program Chair of the ASA Biopharmaceutical Section. He has 90+ publications in statistical and clinical journals.