Bayesian Statistics with Applications to Clinical Trial Design

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This course will review major components of clinical trial conduct, including the formulation of clinical hypotheses and study endpoints, trial design, trial progress monitoring, analysis, and the summary and reporting of results. Bayesian statistics will be reviewed and some Bayesian adaptive designs will be presented along with a software demo. Lastly, flaws in existing clinical drug development will be reviewed with some solutions using Bayesian inference.

Course capacity: 30

Dates & Times

Nov 4, Nov 5, Nov 6; 4-6PM
On campus

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Course Topics

• Major components of clinical trials; different phases of drug trials.
• Overview of Bayesian statistics and philosophy.
• Overview of statistical hypothesis testing and different philosophies on how tests and decisions are made.
• Introduction of Bayesian adaptive designs for early-phase clinical trials.
• Software demo and discussion on flaws of existing drug development.

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Instructor

Yuan Ji, Professor of Biostatistics, Department of Public Health Sciences

Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. His research focuses on innovative Bayesian statistical methods for translational cancer research. Dr. Ji is author of hundreds of publications in peer-reviewed journals including across biomedical and statistical journals. He is the inventor of many innovative Bayesian adaptive designs such as the mTPI and i3+3 designs, which have been widely applied in dose-finding clinical trials worldwide. He received the Mitchell Prize in 2015 by the International Society for Bayesian Analysis. He is an elected fellow of the American Statistical Association.