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Activity Number:
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506
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 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 - #303751 |
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Title:
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Semiparametric Bayes Proportional Odds Models for Current Status Data with Under-Reporting
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Author(s):
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Lianming Wang*+ and David Dunson
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Companies:
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University of South Carolina and Duke University
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Address:
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1523 Greene Street, Columbia, SC, 29208,
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Keywords:
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Cross-sectional ; Interval censored ; Monotone splines ; Nonparametric Bayes ; Survival analysis ; Uterine fibroids
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Abstract:
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Current status data are a type of interval censored event time data in which all the individuals are either left or right censored. For example, our motivation is drawn from a cross-sectional study, which measured whether or not fibroid onset had occurred by the age of an ultrasound exam for each woman. We propose a semiparametric Bayesian proportional odds model in which the baseline event time distribution is estimated nonparametrically by using adaptive monotone splines in a logistic regression model. The proposed approach has the advantage of being straightforward to implement using a simple and efficient Gibbs sampler, while alternative semiparametric Bayes event time models encounter problems for current status data. The model is generalized to allow systematic under-reporting in a subset of the data, and the methods are applied to an epidemiologic study of uterine fibroids.
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