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A linear mixed-effects model for multivariate censored data
1Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis 55455-0378, USA. weip@biostat.umn.edu
Biometrics
|April 28, 2000
Abstract:
We apply a linear mixed-effects model to multivariate failure time data. Computation of the regression parameters involves the Buckley-James method in an iterated Monte Carlo expectation-maximization algorithm, wherein the Monte Carlo E-step is implemented using the Metropolis-Hastings algorithm. From simulation studies, this approach compares favorably with the marginal independence approach, especially when there is a strong within-cluster correlation.