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Activity Number:
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262
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #304355 |
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Title:
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NP Bayes Functional Regression for a PK/PD Semi-Mechanistic Model
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Author(s):
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Michele Guindani*+ and Peter Mueller and Gary L. Rosner and Lena Friberg
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Companies:
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University of New Mexico and The University of Texas M.D. Anderson Cancer Center and The University of Texas M.D. Anderson Cancer Center and Uppsala University
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Address:
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, Albuquerque, NM, 87111,
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Keywords:
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Nonparametric Bayes ; Clustering
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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. We are able 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 is then 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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