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Abstract Details

Activity Number: 253
Type: Contributed
Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #306426
Title: Multivariate Accelerated Failure Time Model with Generalized Estimating Equations
Author(s): Steven Chiou*+
Companies: University of Connecticut
Address: 215 Glenbrook Rd. U-4120, Storrs, CT, 06269,
Keywords: least squares ; AFT ; multivariate survival ; accelerated failure time model

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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