A linear mixed-effects model for multivariate censored data

Wei Pan; Thomas A. Louis

doi:10.1111/j.0006-341X.2000.00160.x

A linear mixed-effects model for multivariate censored data

Research output: Contribution to journal › Article › peer-review

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.

Original language	English (US)
Pages (from-to)	160-166
Number of pages	7
Journal	Biometrics
Volume	56
Issue number	1
DOIs	https://doi.org/10.1111/j.0006-341X.2000.00160.x
State	Published - Mar 2000
Externally published	Yes

Keywords

Buckley-James method
Generalized estimating equations
Least squares
Metropolis-Hastings algorithm
Monte Carlo expectation- maximization
Restricted maximum likelihood estimation

ASJC Scopus subject areas

Statistics and Probability
Biochemistry, Genetics and Molecular Biology(all)
Immunology and Microbiology(all)
Agricultural and Biological Sciences(all)
Applied Mathematics

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10.1111/j.0006-341X.2000.00160.x

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AB - 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.

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KW - Generalized estimating equations

KW - Least squares

KW - Metropolis-Hastings algorithm

KW - Monte Carlo expectation- maximization

KW - Restricted maximum likelihood estimation

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A linear mixed-effects model for multivariate censored data

Abstract

Keywords

ASJC Scopus subject areas

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