CENTER FOR COMPUTATION & TECHNOLOGY

The Bayesian approach is being used increasingly in many areas of biomedical research. In particular, Bayesian sequential analysis provides a natural framework for early phase clinical trials. Traditional dose-finding clinical trial designs aim at identifying an optimal dose of a treatment with a given schedule. Without examining the schedule effects the investigators may be at a risk of misidentifying the best treatment for patients. In this talk, I will briefly introduce Bayesian analysis and discuss one Bayesian clinical trial design we have developed to find the optimal dose-schedule combination that is safe and has the highest efficacy. We propose a Bayesian dynamic model to borrow strength across dose-schedule combinations without making overly restrictive assumptions on the ordering pattern of the schedule effects. We develop a dose-schedule-finding algorithm to sequentially allocate patients to a desirable dose-schedule combination, based on the posterior distribution of toxicity and efficacy at each dose-schedule combination. At the end of the trial, an optimal dose-schedule combination is selected. We conduct extensive simulation studies to examine the operating characteristics of the proposed design under various practical scenarios. Results show that the performance of our design is satisfactory.

Beibei Guo is an assistant professor in the Department of Experimental Statistics at Louisiana State University. She received her PhD degree in Statistics from Rice University in 2010. Then she joined the Department of Biostatistics, University of Texas M.D. Anderson Cancer center, as a postdoctoral fellow. Since August 2013, she has been with the Department of Experimental Statistics at LSU. Her current research interests include Bayesian clinical trial designs, survival analysis, and statistical genomics. She is interested in the development of new statistical methods to address practical problems in biomedical research.