This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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536
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #307949 |
Title:
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A Bayesian Generalized Linear Mixed Model for HIV-1 Vaccine Immune Response with Missing Data
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Author(s):
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Sydeaka Patrice Watson*+ and John Seaman and James Stamey and Bette Korber and Mark Muldoon
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Companies:
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Baylor University and Baylor University and Baylor University and Los Alamos National Laboratory and University of Manchester
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Address:
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Baylor Univ Dept of Statistical Science, Waco, TX, 76798-7140,
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Keywords:
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HIV-1 ;
Bayesian ;
Poisson ;
generalized linear mixed model ;
missing data
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Abstract:
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Genetic diversity is a challenge that the scientific community must overcome before the development of a global HIV-1 vaccine is realized. Two vaccine strategies addressing genetic diversity, namely HIV-1 global consensus envelope sequence (CON-M) and polyvalent vaccine antigens (Mosaic), have been shown to increase the number of positive immune responses in vaccinated monkeys exposed to HIV-1. We compare the CON-M and Mosaic vaccine immune responses in an animal study using a Bayesian generalized linear mixed model for Poisson counts. We compare the conclusions to those resulting from the analogous frequentist case and address missing data considerations.
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Authors who are presenting talks have a * after their name.
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