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Activity Number:
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215
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Type:
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Invited
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Date/Time:
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Tuesday, August 5, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #300113 |
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Title:
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Detecting Safety Signals in Clinical Trials: A Bayesian Perspective
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Author(s):
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H. Amy Xia*+ and Haijun Ma and Bradley P. Carlin
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Companies:
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Amgen, Inc and Amgen, Inc and The University of Minnesota
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Address:
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One Amgen Center Dr, Thousand Oaks, CA, 91320,
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Keywords:
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multiplicity ; signal detection ; clinical trials ; Bayesian hierarchical modeling ; drug safety
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Abstract:
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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. Bayesian hierarchical mixture modeling [Berry and Berry (2004)] is appealing in the following aspects 1) it allows for explicitly modeling the AE data with the existing coding structure; 2) it is attractive in dealing with rare AE data because the model modulates the extremes; and 3) it is flexible to assess the posterior probability of a clinically important difference with different scales. In this presentation, we illustrate the use of Bayesian hierarchical binomial and Poisson mixture models for binary and subject-year adjusted outcomes, respectively. We also show some effective graphics for displaying flagged signals.
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