| Cumulative Incidence Association Models for Bivariate Competing Risks Data. | |
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MedLine Citation:
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PMID: 22505835 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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Association models, like frailty and copula models, are frequently used to analyze clustered survival data and evaluate within-cluster associations. The assumption of noninformative censoring is commonly applied to these models, though it may not be true in many situations. In this paper, we consider bivariate competing risk data and focus on association models specified for the bivariate cumulative incidence function (CIF), a nonparametrically identifiable quantity. Copula models are proposed which relate the bivariate CIF to its corresponding univariate CIFs, similarly to independently right censored data, and accommodate frailty models for the bivariate CIF. Two estimating equations are developed to estimate the association parameter, permitting the univariate CIFs to be estimated either parametrically or nonparametrically. Goodness-of-fit tests are presented for formally evaluating the parametric models. Both estimators perform well with moderate sample sizes in simulation studies. The practical use of the methodology is illustrated in an analysis of dementia associations. |
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Authors:
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Yu Cheng; Jason P Fine |
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Publication Detail:
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Type: JOURNAL ARTICLE |
Journal Detail:
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Title: Journal of the Royal Statistical Society. Series B, Statistical methodology Volume: 74 ISSN: 1369-7412 ISO Abbreviation: - Publication Date: 2012 Mar |
Date Detail:
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Created Date: 2012-4-16 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 100890344 Medline TA: J R Stat Soc Series B Stat Methodol Country: - |
Other Details:
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Languages: ENG Pagination: 183-202 Citation Subset: - |
Affiliation:
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Department of Statistics and Department of Psychiatry, University of Pittsburgh Pittsburgh, PA, USA. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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