Dynamic Borrowing Working Group

We are a collaborative group of statisticians from industry, academia, and regulatory agencies, dedicated to advancing the use, methodology, and regulatory acceptance of dynamic borrowing. Our members include experts in Bayesian methods, evidence integration, clinical trial design, regulatory science, and real‑world data analysis. The group includes representatives from pharmaceutical companies, universities, consultancies, and the FDA. 

Goals of the Scientific Working Group for Dynamic Borrowing

• Advance statistical methodology for dynamic borrowing by Bayesian and hybrid approach with frequentist method to leverage external/historical data, including real world and clinical trial data. 
  
• Address gaps in design and analysis guidance, particularly around prior selection, operating characteristics (including Type I error assessment and false decision-making rate), and simulation-based evaluation to inform regulatory and scientific decision making.
  
• Facilitate cross sector collaboration among statisticians from industry, academia, and regulatory agencies to align expectations and promote best practices for dynamic borrowing.
  
• Enable efficient and scientifically grounded drug development, especially in areas where external information can be meaningfully incorporated (e.g., pediatrics, rare diseases).
  
• Educate the broader statistical and cross-functional community through white papers, conference presentations, tutorials, open-source tools, and other dissemination channels. 

Co-Chairs

• Jerry Li (Bausch Health)
• Sabrina Wan (Merck)
• Inna Perevozskaya (BMS)