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Abstract Details
Activity Number:
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253
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #306426 |
Title:
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Multivariate Accelerated Failure Time Model with Generalized Estimating Equations
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Author(s):
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Steven Chiou*+
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Companies:
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University of Connecticut
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Address:
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215 Glenbrook Rd. U-4120, Storrs, CT, 06269,
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Keywords:
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least squares ;
AFT ;
multivariate survival ;
accelerated failure time model
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Abstract:
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The accelerated failure time model has not been as widely used as the Cox relative risk model mainly due to computation difficulties. Recent developments in least squares estimation and induced smoothing estimating equations provide promising tools to make the accelerate failure time models more attractive in practice. This paper focuses on multivariate accelerated failure time models. We propose a generalized estimating equation approach to account for the multivariate dependence through working correlation structures. The marginal error distributions can be either identical as in sequential event settings or different as in parallel event settings. Some regression coefficients can be shared across margins as needed. The initial estimator is a rank-based estimator with Gehan's weight, but obtained from an induced smoothing approach with computation ease. The resulting estimator are consistent and asymptotically normal, with a variance estimated through a multiplier resampling method. In a simulation study, our estimator is shown to be up to three times as efficient as the initial estimator, especially with stronger multivariate dependence and heavier censoring percentage.
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Authors who are presenting talks have a * after their name.
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