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Activity Number:
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424
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2004 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #300681 |
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Title:
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A Queuing Model for Chronic Recurrent Conditions
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Author(s):
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Catherine Crespi*+ and William G. Cumberland and Sally Blower
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Companies:
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University of California, Los Angeles and University of California, Los Angeles and University of California, Los Angeles
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Address:
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School of Public Health, Los Angeles, CA, 90095-1772,
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Keywords:
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stochastic model ; queueing process ; hidden Markov model ; virus ; Bayesian inference ; Markov chain Monte Carlo
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Abstract:
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In many chronic physiological conditions, subjects alternate between an active and inactive state, and sojourns into the active state may involve multiple lesions, infections, or other recurrences with different times of onset and resolution. We present a biologically interpretable model of such chronic, recurrent conditions based on a queueing process. The model has a birth-death process describing recurrences and a semi-Markov process describing the alternation between active and inactive states. We accommodate individual heterogeneity and covariates using a random effects model, and simulate the posterior distribution of unknowns using a Markov chain Monte Carlo algorithm. Application to herpes simplex virus shows how the method can characterize the biology of the condition and estimate treatment efficacy.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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