A linear mixed-effects model for multivariate censored data

被引：19
|
作者：
Pan, W [1 ]
Louis, TA [1 ]
机构：
[1] Univ Minnesota, Sch Publ Hlth, Div Biostat, Minneapolis, MN 55455 USA
来源：
BIOMETRICS | 2000年 / 56卷 / 01期
关键词：
Buckley-James method; generalized estimating equations; least squares; Metropolis-Hastings algorithm; Monte Carlo expectation-maximization; restricted maximum likelihood estimation;
D O I：
10.1111/j.0006-341X.2000.00160.x
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
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.
引用
收藏
页码：160 / 166
页数：7
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