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Abstract Details
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Activity Number:
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162
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #302685 |
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Title:
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A Hierarchical Bayesian Model for Multivariate Timeline Follow-Back Histories
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Author(s):
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Adam King*+ and Robert E. Weiss
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Companies:
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University of California at Los Angeles and University of California at Los Angeles
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Address:
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641 Gayley Ave., Los Angeles, CA, 90024,
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Keywords:
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Bayesian ;
Hierarchical ;
Longitudinal ;
Multivariate
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Abstract:
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Timeline follow-back (TLFB) is a method for retrospectively eliciting histories of behaviors and circumstances from study subjects. For each such time-dependent trait of the subject, time segments on which that trait was constant are recorded along with covariates describing that trait on that time segment. We propose a hierarchical Bayesian model for this data structure, along with supporting computational tools and graphics. We apply these methods to lifetime TLFB histories of illicit drug use and related traits and experiences of 508 subjects.
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