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Activity Number:
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103
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Type:
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Contributed
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Date/Time:
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Monday, August 7, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #305813 |
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Title:
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Bayesian Variable Selection in Cox Models
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Author(s):
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Naijun Sha*+ and Mahlet G. Tadesse and Marina Vannucci
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Companies:
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The University of Texas at El Paso and University of Pennsylvania and Texas A&M University
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Address:
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500 W. University Ave., El Paso, TX, 79968,
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Keywords:
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Bayesian variable selection ; Cox model ; survival analysis ; censored time ; MCMC
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Abstract:
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In this paper, we investigate variable selection methods for Cox's proportional hazard model. We develop selection methods that allow for censored data. Our methods lead to simultaneously estimates of the survival function as well as to the identification of the factors that affect the survival outcome. We handle the problem of selecting a few predictors among the prohibitively vast number of variables through the introduction of a binary exclusion/inclusion latent vector. This vector is updated via an MCMC technique to identify promising models. We describe strategies for prosterior inference and explore the performance of the methodology with simulated and real datasets.
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