Integrating diverse evidence sources in clinical research: bridging RCTs and RWD

Randomized clinical trials remain the gold standard for estimating treatment effects, but they are often small, selective, and short in follow-up. Real-world data from electronic health records, claims, and registries can fill some of these gaps, and AI/ML tools are increasingly used to extract features and build flexible models. The hard problem is not access to more data; it is combining sources without letting bias from observational data undermine valid inference.

This talk discusses how to integrate randomized trials and real-world data with statistical care. I will start with the roles of trials, real-world evidence, and AI in today’s regulatory landscape, then focus on hybrid controlled trials that borrow external or real-world controls to improve efficiency. A central question is how to borrow comparable external controls and down-weight or discard those that are not comparable. I will present a set of bias-aware methods, from simple test-then-pool rules to selective borrowing and randomization-based tests, that aim to gain power while protecting type-I error. I will close with what is ready for practice, and what remains open.

Shu Yang is a Professor of Statistics, Goodnight Early Career Innovator, and University Faculty Scholar at North Carolina State University. She received her Ph.D. in Applied Mathematics and Statistics from Iowa State University and completed her postdoctoral training at the Harvard T.H. Chan School of Public Health. Her research focuses on causal inference, real-world evidence, and data integration, particularly in the context of comparative effectiveness research in health studies. Dr. Yang has served as Principal Investigator on multiple large-scale research grants from the NSF, NIH (R01), and FDA (U01).

Host: Nan Lin