TITLE: Practical Hierarchical Bayesian Modeling
INSTRUCTOR: Fang Chen, SAS Institute Inc.



This course reviews the basic concepts of Bayesian hierarchical models and focuses on using software to fit multilevel random-effects models. We put emphasis on computational tools, and the course objectives are to familiarize attendees with practical essentials of the Bayesian paradigm in hierarchical modeling. Topics that will be presented in depth include choices and impact of prior distributions in random-effects models, fitting multilevel models using SAS (PROC BGLIMM and PROC MCMC), various types of posterior predictions, and biopharmaceutical examples that utilize multisource information, such as use hierarchical models as a way to borrow from historical data, PKPD models, and adaptive basket trial design.

Instructor’s Bio:

Fang Chen(PhD in Statistics from Carnegie Mellon University). Dr. Chen is Director of Analytical Software Development at SAS Institute Inc. and a Fellow of the American Statistical Association. He manages the development of statistical software for SAS/STAT®, SAS/QC®, and analytical components that drive SAS® Visual Statistics software. He is alsoresponsible for the development of Bayesian analysis software and the MCMC procedure at SAS. Evgeny Degtyarev (Master’s in


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