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Activity Number:
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229
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Type:
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Invited
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Date/Time:
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Tuesday, August 8, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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| Abstract - #305219 |
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Title:
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Nonparametric Analysis of Multivariate Competing Risks Data
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Author(s):
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Jason P. Fine*+
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Companies:
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University of Wisconsin-Madison
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Address:
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Departments of Statistics and Biostatistics, K6-420, CSC, Madison, WI, 53792,
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Keywords:
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bivariate association measures ; empirical processes ; uniform convergence properties ; cause-specific incidence and hazard functions ; nonparametric estimation
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Abstract:
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While nonparametric analyses of bivariate failure times have been studied widely, nonparametric analyses of bivariate competing risks data have not been investigated. Such analyses are important in familial association studies, where multiple interacting failure types invalidate nonparametric analyses for independently censored clustered data. We develop nonparametric estimators for the bivariate cause-specific hazards function and the bivariate cumulative incidence function, natural extensions of their univariate counterparts, which make no assumptions about the dependence of the risks. The estimators are shown to be uniformly consistent and converge weakly to Gaussian processes. Summary association measures are proposed and yield formal tests of independence in relative pairs. An illustrative analysis of dementia onset associations in the Cache County Study on Aging will be presented.
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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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