A linear mixed-effects model for multivariate censored data

Wei Pan, Thomas Louis

Research output: Contribution to journalArticle

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 languageEnglish (US)
Pages (from-to)160-166
Number of pages7
JournalBiometrics
Volume56
Issue number1
StatePublished - Mar 2000
Externally publishedYes

Fingerprint

Multivariate Failure Times
Linear Mixed Effects Model
Metropolis-Hastings Algorithm
Failure Time Data
Monte Carlo Algorithm
Expectation-maximization Algorithm
Multivariate Data
Censored Data
Regression
Simulation Study
Monte Carlo method
Independence

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

Cite this

A linear mixed-effects model for multivariate censored data. / Pan, Wei; Louis, Thomas.

In: Biometrics, Vol. 56, No. 1, 03.2000, p. 160-166.

Research output: Contribution to journalArticle

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