Abstract Details
Activity Number:
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608
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Type:
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Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 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 - #309750 |
Title:
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A Bayesian Joint Hierarchical Model for Long-Term Multiple Substance Use and Recovery from Substance Use
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Author(s):
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Li-Jung Liang*+ and Chi-hong Tseng and Yih-Ing Hser
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Companies:
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UCLA and UCLA and UCLA
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Keywords:
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Joint Model ;
Two-Stage Model ;
Poly-drug Use
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Abstract:
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Research on how the patterns of addicts' early-period substance use can predict their recovery from long-term substance use, using data from natural history interview studies, is limited. We propose to use a Bayesian joint hierarchical model to investigate the association between patterns of addicts' early-period substance use (longitudinal) and time to recovery from substance use. This approach allows us to properly account for the correlations among multiple drugs within subjects and to provide efficient estimates for the association between time to recovery and long-term use of multiple drugs. A 33-year follow-up study was used to demonstrate our approach.
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Authors who are presenting talks have a * after their name.
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