This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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303
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 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 - #307937 |
Title:
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Nonparametric Bayesian Methods for Measurement Error Problem in Matched Case-Control Studies
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Author(s):
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Nels Johnson*+ and Inyoung Kim
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Companies:
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Virginia Tech and Virginia Tech
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Address:
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, Blacksburg, VA, 24060, United States of America
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Keywords:
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Conditional likelihood ;
Dirichlet process ;
Matched case-control ;
Nonparametric Bayesian
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Abstract:
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In epidemiological research, matched case-control studies are popular. Measurement error of covariates is a modeling problem that often needs to be addressed. However, there is no statistical approach to handle both problems together. We propose a nonparametric Bayesian method for measurment error models in matched case-control studies, modeling the measurment error as a Dirichlet process mixture. We compare our method with a parametric Bayesian method and a frequentist conditional likelihood method. We show that our method performs better using a simulation study. We also demonstrate the advantages of our approach on a 1-5 matched case-crossover study in public health epidemiology.
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