Bayesian Hierarchical Modeling for Detecting Safety Signals in Clinical Trials
*Bradley P. Carlin, University of Minnesota *Haijun Ma, Amgen *H. Amy Xia, Amgen, Inc.
Detection of safety signals from routinely collected adverse event data in clinical trials is critical in drug development. How to deal with the multiplicity issue and rare adverse event (AE) data in such a setting is a challenging statistical problem. Without multiplicity considerations, there is a potential for an excess of false positive signals. On the other hand, traditional ways of adjusting for multiplicity often fail to flag important signals. Bayesian hierarchical modeling is appealing in dealing with this problem. In this presentation, we illustrate the use of Bayesian hierarchical binomial and Poisson models for binary and subject-year adjusted outcomes, respectively by explicitly modeling the existing AE coding structure. We further extend the work by Berry and Berry  by providing guidance on how to choose the signal detection threshold in an effort to balance the type I and type II error rates. We also show some effective graphics for displaying flagged signals when analyzing hundreds or thousands of AEs.
April 30 - May 22, 2013
Invited Abstract Submission Open
June 4, 2013
Online Registration Opens
August 9 - August 23, 2013
Invited Abstract Editing
August 23, 2013
Short Course materials due from Instructors
August 26, 2013
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC