title: A linear mixed-effects model for multivariate censored data.
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.
authors.affiliation: Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis 55455-0378, USA. weip@biostat.umn.edu
authors.forename: W
authors.initials: W
authors.lastname: Pan
authors.forename: T A
authors.initials: TA
authors.lastname: Louis
mesh_terms: D000465:Algorithms; D000818:Animals; D001699:Biometry; D016014:Linear Models; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D009374:Neoplasms, Experimental / Q000139:chemically induced; D051381:Rats; D012044:Regression Analysis