Duke University in collaboration with North Carolina State University (NCSU) have been awarded up to $2.6 million by the U.S. Food and Drug Administration. The grant, spanning three years from September 1, 2023, to August 31, 2026, aims to innovate statistical methods that boost the efficiency and robustness of clinical trials. This is done by incorporating real-world data and taking into account hidden biases.
The initiative will be jointly led by Xiaofei Wang, Ph.D., professor in the Duke Department of Biostatistics and Bioinformatics, and Shu Yang, Ph.D., associate professor in the Department of Statistics at North Carolina State University.
Prominent contributors to this venture encompass Shein-Chung Chow, Ph.D., professor in the Duke Department of Biostatistics and Bioinformatics, Thomas Stinchcombe, MD, professor in the Duke Department of Medicine, Roee Gutman, associate professor in the Department of Statistics at Brown University, and Mingyang Shan, Doug Faries, and Kristin Sheffield of Eli Lilly & Company.
Additionally, the grant will provide support for graduate students and postdoctoral research associates. This U01 grant emphasizes strengthening collaboration between FDA and investigators from academics and industry.
"Receiving support from the FDA is not just an affirmation of our endeavors but a testament to the collective vision of harnessing real-world data for transformative regulatory decisions" Yang said. "We're humbled and honored to pave the way toward a future where each patient's data enriches the evidence for treatments,” Yang continued.
With the ongoing evolution in health data acquisition, relevant real-world data (RWD) are being utilized to enhance clinical trial evaluations, especially concerning safety and efficacy. However, using these external controls has its challenges, such as potential concealed biases. The investigators in this endeavor aim to craft a unique sensitivity analysis structure. This will evaluate the impact of these biases while devising robust methodologies to counterbalance and accommodate data with hidden bias and variances. The team will also release user-centric software tools and templates for statistical analyses, coupled with practical examples and case studies. The end goal is to harness real-world evidence more effectively in regulatory decisions.
This funding opportunity is a direct response to the FOA RFA-FD-23-025, which seeks to champion the application of Real-World Data in generating practical evidence for regulatory decision-making. The primary objectives of this FOA are to improve the quality and/or use of RWD, promote a better understanding of RWE study designs, and develop specific tools to evaluate aspects of RWE generation.