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Activity Number:
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311
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Type:
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Invited
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Date/Time:
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Tuesday, July 31, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistics in Epidemiology
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| Abstract - #307903 |
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Title:
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Semiparametric Bayesian Latent Trajectory Models
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Author(s):
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Amy H. Herring*+ and David B. Dunson
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Companies:
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The University of North Carolina at Chapel Hill and National Institute of Environmental Health Sciences
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Address:
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CB 7420, Chapel Hill, NC, 27599,
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Keywords:
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Bayesian ; latent variable ; exposure assessment ; disinfection by-product ; joint model
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Abstract:
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Exposures of interest often may vary over relatively long periods of time. Latent trajectory models (LTMs) characterize longitudinal exposure data using a finite mixture of curves. We address uncertainty in the number of latent classes and in the form of the class-specific curves using a semiparametric Bayesian approach, which is generalized to allow joint nonparametric modeling with a multivariate response. The proposed approach allows the response distribution to be unknown and varying with trajectory class. An MCMC algorithm is developed for posterior computation. The methods are motivated by an epidemiologic study of water quality and pregnancy outcomes and are compared to analyses using simpler exposure summaries commonly used in epidemiologic studies.
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