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Activity Number:
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420
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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ENAR
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| Abstract - #308839 |
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Title:
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Modeling Associations via Incidence Functions with Bivariate Competing Risks Data
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Author(s):
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Yu Cheng*+ and Jason Fine
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Companies:
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University of Pittsburgh and University of Wisconsin-Madison
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Address:
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2717 Cathedral of Learning, Pittsburgh, PA, 15260,
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Keywords:
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Cause-specific failure patterns ; Empirical processes ; Nonparametric estimation ; Time-dependent association
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Abstract:
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Frailty models are frequently used to analyze clustered survival data and evaluate within-cluster associations. However, they are seldom used in bivariate competing risks settings because of the multiple interacting failure types. To address this issue, we focus on a nonparametrically identifiable quantity: cumulative incidence function (CIF). Frailty models are constructed expressing the bivariate CIF in terms of its marginals based on some improper random variables. Estimating equations are proposed to estimate the unknown association parameter involved in frailty models. The large sample properties of the association parameter estimators are established using empirical processes techniques and their practical performances are studied by Monte-Carlo simulations. We illustrate their practical utility by an analysis of dementia in the Cache County Study.
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