Modern statistical designs such as Bayesian adaptive designs have made major impact on clinical trials. Advanced designs may reduce trial duration, reduce sample size, or increase trial success probability. However, these designs are usually complex and require intensive simulations to calibrate for individual trials. There is a strong barrier to propose and implement these designs in practice due to lack of specialized software.
This talk introduces a new Software as a Service (SaaS) — software on the cloud — called U-Design that makes it practical to apply advanced statistical designs. Specifically, the U-Design platform simplifies application of advanced statistical modeling, and streamlines and simplifies the formidable process of reading literature, comparing existing methods via extensive simulation, calibrating modeling parameters for optimization, and generating final statistical report for submission and protocol writing. Users can begin designing more efficient trials from virtually any device in a matter of minutes.
- New ways that innovative designs may save time and resources for drug development.
- How user-friendly software can overcome practical barriers to allow comparison and assessment of complex and adaptive designs.
- Tactics to save time and enforce reproducibility on generating intelligent statistical reports for adaptive designs.
Yuan Ji, Ph.D. is currently the Professor of Biostatistics (with tenure) at The University of Chicago. He spent 9 years at The University of Texas M.D. Anderson Cancer Center, holding tenure-track and tenured faculty positions.
He is internationally known for his work on designs of dose-finding trials, adaptive dose insertion, seamless and overlapping phase I/II trials, immune-oncology studies, and subgroup enrichment approach. He is also an expert in bioinformatics and computational biology, with a deep understanding of translational medicine.
He has published over 100 peer-reviewed papers in top journals across different scientific disciplines, including Nature Methods, Journal of Clinical Oncology, Journal of the National Cancer Institute.