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Cancer Biostatistics Monthly Seminar

October 19, 2023 @ 12:00 pm - 1:00 pm UTC

Photo of Yuan Ji

PAM-HC: A Bayesian Nonparametric Construction of Hybrid Control for Randomized Clinical Trials Using External Data

Yuan Ji, PhD

Professor of Biostatistics

Department of Public Health Sciences

University of Chicago

Dr. Yuan Ji graduated from Fudan University with a bachelor in Mathematics, University of Wisconsin – Madison with a PhD in Statistics. He spent 9 years at The University of Texas M. D. Anderson Cancer Center as Assistant and Associate Professor in Biostatistics and Bioinformatics. Currently, Dr. Yuan Ji is Professor of Biostatistics at The University of Chicago. Dr. Ji is author of over 200 publications in peer-reviewed journals, conference papers, book chapters, and abstracts. He is the inventor of many innovative Bayesian adaptive designs such as the mTPI and mTPI-2 designs, which have been widely applied in dose-finding clinical trials. Dr. Ji is an elected fellow of ASA.


It is highly desirable to leverage information from external data to augment a current control arm in a randomized clinical trial, in settings where the sample size for the control arm is limited. However, a main challenge in borrowing information from external data source is to accommodate potential heterogeneous subpopulations across the external and trial data. We introduce a nonparametric Bayesian model called Plaid Atoms Model (PAM) to identify overlapping and unique subpopulations across datasets, with which we properly borrow information from the external data to augment the control arm in the clinical trial. This forms a hybrid control (HC) and leads to more precise estimation of treatment effects due to augmented information borrowed from external data. Simulation studies demonstrate the robustness of the new method, and an application to an Atopic Dermatitis dataset shows improved treatment effect estimation.

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Meeting ID: 891 0722 7494

Passcode: aEb1izA7

This group meets monthly from Noon – 1 p.m. The meeting agenda includes a presentation, programmatic announcements, and updates from Cancer Center program leaders.


October 19, 2023
12:00 pm - 1:00 pm UTC
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Nelson Chen
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