JSM 2005 - Toronto

Abstract #303979

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 266
Type: Contributed
Date/Time: Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor: Biopharmaceutical Section
Abstract - #303979
Title: A Bayesian Pharmacokinetic Metaanalysis for Published Sample Mean Data: A Ketoconazole-Midazolam Example in Drug-drug Interaction Research
Author(s): Lang Li*+ and Menggang Yu and Aroonrut Lucksiri and Stephen D. Hall
Companies: Indiana University and Indiana University and Indiana University and Indiana University
Address: 1050 Wishard Boulevard, Indianapolis, IN, 46202, United States
Keywords: Bayesian model ; drug-drug interaction ; meta-analysis ; pharmacokinetics ; prediction
Abstract:

A metaanalysis from multiple data resources is an important method to obtain a comprehensive understanding of a drug's pharmacokinetics (PK). In drug-drug interaction research, often PK data on inhibitor/inducer or substrate's PK are not directly available from clinical studies, though there always exist rich published PK information in the literature. For example, sample mean plasma drug concentrations and their standard deviations have been reported routinely. However, no statistical metaanalysis method was developed to analyze these data and build up a drug's PK model. In this paper, an innovative general Bayesian metaanalysis model framework was established to analyze multiple published sample mean datasets. It is a three-level hierarchical model, in which a study-specific sample mean model is the first level, a random-effects model connecting different PK studies is the second level, and all priors of PK parameters are specified in the third level. This Bayesian model has the advantage of adapting informative prior when some PK parameters' bounds are known.

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Revised March 2005