Abstract Details
Activity Number:
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414
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Type:
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Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #313519
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Title:
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Modeling Multiple Types of Outcomes Using SASĀ® PROC MCMC
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Author(s):
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Sitaram Vangala*+ and Li-Jung Liang and Yih-Ing Hser
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Companies:
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University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles Integrated Substance Abuse Programs
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Keywords:
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Bayesian model ;
Zero-inflated model ;
Longitudinal study ;
Substance use ;
Incarceration
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Abstract:
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Many public health studies involve multiple response types. In this paper, we develop various joint models to account for correlations between repeated measurements (e.g., zero-inflated counts) and binary outcomes, leading to more precise estimates. We adopt a Bayesian approach, using SASĀ® PROC MCMC, a recently introduced Markov chain Monte Carlo sampler, to implement the proposed models. A long-term follow-up study of narcotics addicts is used to illustrate our approach.
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Authors who are presenting talks have a * after their name.
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