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 |
| State | Published - Mar 2000 |
| Externally published | Yes |
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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 journal › Article
}
TY - JOUR
T1 - A linear mixed-effects model for multivariate censored data
AU - Pan, Wei
AU - Louis, Thomas
PY - 2000/3
Y1 - 2000/3
N2 - 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.
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.
KW - Buckley-James method
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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UR - http://www.scopus.com/inward/citedby.url?scp=0034104426&partnerID=8YFLogxK
M3 - Article
C2 - 10783791
AN - SCOPUS:0034104426
VL - 56
SP - 160
EP - 166
JO - Biometrics
JF - Biometrics
SN - 0006-341X
IS - 1
ER -