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Abstract Details
Activity Number:
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240
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract - #303191 |
Title:
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Nonparametric Approaches to Assess the Risk of QT Prolongation of an Investigational New Drug
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Author(s):
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Kristin Ashley Linn*+ and Sujit Kumar 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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Department of Statistics, Cary, NC, 27519,
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Keywords:
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Bayesian inference ;
Monte Carlo simulation ;
QT interval ;
QT prolongation ;
Linear mixed models ;
Nonparametric
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Abstract:
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The QT interval is the portion of the heart's electrical cycle that corresponds to ventricular depolarization and subsequent repolarization. Some non-antiarrhythmic drugs may cause QT interval prolongation, which is associated with a potentially fatal condition called torsades de pointes. In this paper, we use a Gaussian process framework to estimate the maximum difference from baseline of the corrected QT interval (QTc). The mean of the Gaussian process is approximated by a sequence of Bernstein polynomials, which is shown to result in a simple linear mixed model framework. The maximum difference between the mean functions is then estimated to characterize the effect of treatment on the QTc interval. The results of a simulation study demonstrate that our proposed method has several advantages over existing methods including decreased bias and mean squared error. We apply our method to a real data set obtained from a 'thorough QT/QTc' study conducted by GlaxoSmithKline.
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