|
Activity Number:
|
378
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 1, 2007 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Biopharmaceutical Section
|
| Abstract - #309828 |
|
Title:
|
Bayesian Hierarchical Models for Detecting Safety Signals in Clinical Trials
|
|
Author(s):
|
Hong A. Xia*+ and Haijun Ma
|
|
Companies:
|
Amgen Inc. and Amgen Inc.
|
|
Address:
|
One Amgen Ctr Dr, Thousand Oaks, CA, 91320,
|
|
Keywords:
|
Bayesian Hierarchical Models ; Clinical Trials ; Drug Safety ; Multiplicity ; Signal Detection
|
|
Abstract:
|
Detection of safety signals from routinely collected adverse event data in clinical trials is a critical task in drug development. How to deal with the multiplicity issue 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 may fail to flag important signals too often. Bayesian hierarchical modeling brings some promise. It allows for explicitly modeling AEs with the existing coding structure so that they can borrow strength from each other depending on the actual data. Following the work by Berry and Berry (2004), we implement the Bayesian hierarchical mixed model for subject incidence and extend this model for exposure adjusted incidence or exposure adjusted event rate, using drug exposure time between patients.
|
