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 | |
| 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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Cite this
Pan, W., & Louis, T. A. (2000). A linear mixed-effects model for multivariate censored data. Biometrics, 56(1), 160-166. https://doi.org/10.1111/j.0006-341X.2000.00160.x