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Activity Number:
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366
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Type:
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Contributed
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Date/Time:
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Wednesday, August 6, 2008 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #301968 |
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Title:
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A Flexible Class of Models for Data Arising from a 'Thorough QT/QTc Study'
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Author(s):
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Suraj P. Anand*+ and Sujit Ghosh
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Companies:
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North Carolina State University and North Carolina State University
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Address:
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2300 Avent Ferry Road, Raleigh, NC, 27606,
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Keywords:
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Bayesian Model Selection ; DIC ; ICH E14 ; MCMC ; QT/QTc ; thorough QT/QTc Study
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Abstract:
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The standard methods for analyzing data arising from a "thorough QT/QTc study" are based on multivariate normal models with common variance structure for both drug and placebo. Such modeling assumptions may be violated and when the sample sizes are small the statistical inference can be sensitive to such stringent assumptions. This article proposes a flexible class of parametric models to address the above mentioned limitations of the currently used models. A Bayesian methodology is used for data analysis and models are compared using the deviance information criteria (DIC). Superior performance of the proposed models over the current models is illustrated through a real data set obtained from a GlaxoSmithKline conducted thorough QT study.
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