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Abstract Details
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Activity Number:
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229
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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 : 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 - #302316 |
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Title:
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A Joint Markov Chain Model for the Association of Two Longitudinal Binary Processes
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Author(s):
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Catherine M. Crespi*+ and Sherry Lin
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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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, , ,
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Keywords:
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Markov model ;
longitudinal data ;
joint model ;
nonhomogeneous
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Abstract:
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In some longitudinal studies, several related processes are measured, and interest focuses on their temporal association. We propose a joint model for two longitudinal binary processes in which each process is modeled as a nonhomogeneous first-order Markov chain, where the time-dependent transition intensities for each chain depend upon the current and/or past states of the other chain. The joint posterior distribution of the model parameters is obtained using Markov chain Monte Carlo methods. We apply our model to longitudinal data on viral shedding collected from individuals infected with type 2 herpes simplex virus, in which data for each individual were collected from two regions of the genital mucosa.
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