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Abstract Details
Activity Number:
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603
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Health Policy Statistics
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Abstract - #300560 |
Title:
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A Bayesian Approach for the Analysis of Latent Stage-Sequential Process: Timing of Drinking Onset and Sequential Patterns of Alcohol Use
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Author(s):
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Hwan Chung*+
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Companies:
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Ewha Womans University
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Address:
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11-1 Daehyun-dong, Seoul, International, 120-750, Korea
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Keywords:
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early drinking onset ;
label switching ;
latent class profile analysis ;
MCMC ;
stage-sequential process ;
under-age drinking
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Abstract:
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Research on the sequential development of alcohol use can be challenging in part because the stage of drinking behavior at a certain time point may not be directly observable. In this context, a latent class analysis (LCA) can provide a set of principles for systematic identification of homogeneous subgroups. We propose an LCA approach, referred to as a latent class-profile analysis (LCPA), for investigating the dependency of stage-sequential patterns of adolescent drinking behavior on timing of drinking onset. Analyses indicate that early drinking onset is related to increased alcohol use. Among adolescents aged 12-14, those who already have tried alcohol are more likely to become heavy drinkers compared to their late-onset counterparts. Parameters for LCPA are estimated by Bayesian method via Markov chain Monte Carlo (MCMC). This paper proposes a dynamic-dependent prior for LCPA to deal with label switching. We argue that when conventional Bayesian estimates behave erratically, problems often may be alleviated with a small amount of prior input for the LCPA model.
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Authors who are presenting talks have a * after their name.
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