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Activity Number:
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372
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #309496 |
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Title:
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Meta-Analysis Based on Summarized Data with Application to Drug-Drug Interaction
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Author(s):
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Lang Li*+
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Companies:
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Indiana University
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Address:
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410 West 10th street, Indianapolis, IN, 46202,
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Keywords:
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Bayesian Model ; pharmacokinetics ; drug-drug interaction ; meta-analysis ; Monte-Carlo Markov Chain
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Abstract:
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An innovative DDI prediction method based on a three-level hierarchical Bayesian meta-analysis model is developed. The first level model (study-specific) recovers study specific PK parameters and between subject variations. The second level model (between-study specific) describes the between study variations, and the third level model (prior distributions) summarizes the prior knowledge of PK parameters and their variances. Seven ketoconazole and five midazolam studies are analyzed through this model, and their in-vivo DDI (measure with area under the concentration curve ratio) is predicted. Monte Carlo Markov chain (MCMC) is used for generating posterior samples of all parameters and DDI predictions. The performance of this model is validated through the statistical simulations.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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