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Activity Number:
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192
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 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 - #309515 |
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Title:
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Bayesian Multivariate Growth Curve Latent Class Models for Mixed Outcomes
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Author(s):
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Benjamin E. Leiby*+ and Mary D. Sammel and Thomas R. Tenhave and Kevin G. Lynch
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Companies:
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Thomas Jefferson University and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
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Address:
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1015 Chestnut St, Philadelphia, PA, 19107,
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Keywords:
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Latent Variable ; Latent Class ; Multivariate ; Longitudinal ; Growth Mixture
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Abstract:
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In studies of complex diseases, multiple outcomes are often used to adequately capture information about disease severity. The disease population may also have distinct subgroups and identification of these subgroups is of interest as it may assist clinicians in providing appropriate treatment or in developing accurate prognoses. We propose Bayesian models that group subjects based on multiple continuous, binary, ordinal or count outcomes measured repeatedly over time. These groups or latent classes are defined by distinctive longitudinal profiles of a latent variable which is used to summarize the multivariate outcomes at each point in time. The mean growth curve for the latent variable in each class defines the features of the class. We apply the model to data from a clinical trial evaluating the efficacy of Bacillus Calmette-Guerin in treating symptoms of Interstitial Cystitis.
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