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Abstract Details
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Activity Number:
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553
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Type:
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Invited
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Date/Time:
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Wednesday, August 3, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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WNAR
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| Abstract - #300325 |
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Title:
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Nonparametric Bayes Functional Regression for A PK/PD Semimechanistic Model
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Author(s):
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Michele Guindani*+ and Peter Mueller and Gary Rosner
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Companies:
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The University of Texas MD Anderson Cancer Center and The University of Texas MD Anderson Cancer Center and The Johns Hopkins University
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Address:
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Department of Biostatistics - Unit 1411, Houston, TX, 77025,
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Keywords:
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Bayesian Nonparametrics ;
Dirichlet Process ;
PK/PD models
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Abstract:
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In Clinical pharmacology, a major goal is to study the quantitative prediction of drug effects. Complex pharmacokinetic-pharmacodynamic (PK/PD) models with feedback and transition effects have been recently developed, for example to estimate the time course of myelosupression. These models typically involve the presence of covariate dependent parameters. We propose a coherent NP Bayes probabilistic framework for the analysis of these models. Our goal is to use the information from the individuals' time courses and covariates to provide a clustering of patients according to their PK/PD profiles. This information could then be used to predict the PD time course for a patient on the basis of the observed PK profile, as well as deal with missing data.
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