Title: Impactful Collaborative Research: From rhBMP-2 to Ferabright™

Presented by Rongwei (Rochelle) Fu, Professor of Biostatistics, Oregon Health & Science University-Portland State University

Recipient of the 2025 UConn Statistics Department Distinguished Alumnus Award

DATE: Wednesday, April 22, 2026, 3:30 PM, AUST 434

Meeting Link: WebEx link

Coffee will be available at 3:00 PM in the Noether Lounge (AUST 326)

Bio: Dr. Rongwei (Rochelle) Fu is a proud alumna and earned dual PhDs in Plant Science and Statistics from University of Connecticut in 2000 and 2003, separately. Currently she is a professor of biostatistics and director of the biostatistics education program in Oregon Health & Science University (OHSU)-Portland State University (PSU) School of Public Health. She’s also a professor in the Division of Informatics, Clinical Epidemiology & Translational Data Science in Department of Medicine. She became a fellow of American Statistical Association in 2017. Dr. Fu is a prolific collaborative team scientist with more than 300 peer-reviewed publications, a H-index of 89 and ~ 34800 citations. Her research interests include systematic reviews and complex meta-analysis, mixture models, clinical trials, Bayesian models for biomarker and risk prediction, and novel application of statistical methods to biomedical and public health research.

Abstract: Biostatistical considerations are essential to biomedical research. This presentation will share two case studies. The first, the rhBMP-2 study, is a systematic review and individual participant meta-analysis after FDA issued a public health notification of life-threatening complications associated with off-label use of rhBMP-2 in cervical spine fusion. This work focused on evaluating available evidence and reporting bias. The second Ferabright™ study will describe a unique experience working with FDA to identify an appropriate study design that eventually led to ferumoxytol approval as an MRI contrast agent. Key design considerations for the five versions of intermediate and final study designs (e.g., non-inferiority vs. superiority, comparison group, multiple adjustments and “win” criteria, optimal use of existing data) will be discussed.