Bayesian Statistics with Applications in Clinical Trials in the New Era
Thank you for your interest in the recording of our recent webinar. Professor Yuan Ji will review Bayesian statistical philosophies and their applications to early and late-phase clinical trials. The goal is to provide a Bayesian perspective on statistical modeling and decision making for clinical trials. In the early phase drug development, more nimble modeling approaches are typically allowed due to the exploratory nature. However, errors made in early phase have grave impact to drug development since these errors lead to major waste of resources.
During this webinar Professor Yuan Ji will discuss:
- How Bayesian modeling can help mitigate the potential errors in decision making in early-phase expansion cohort trials. In late phase, due to current regulatory constraints, statistical errors are expressed in frequentist type I and type II error rates.
- How Bayesian and frequentist sequential decision making are different, especially through interim analyses. Various Bayesian approaches will be presented.
Yuan Ji
Yuan Ji, Ph.D. is an Executive Advisor to Cytel and is currently the Professor of Biostatistics (with tenure) at The University of Chicago. Prior to this, he has spent several years at The RICE University and The University of Texas M.D. Anderson Cancer Center, holding tenure-track and tenured faculty positions.
He is internationally known for his work on Bayesian designs and tools, adaptive trial designs and implementation in dose-finding trials, 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 150 peer-reviewed papers in top journals across different scientific disciplines, including Nature Methods, Journal of Clinical Oncology, Journal of the National Cancer Institute.