Flexible modelling of the covariance matrix in a linear random effects model

Biometrical Journal

Volumn 42, Issue 7, 2000, Pages 807-822

  Lesaffre, Emmanuel ^a   Todem, David ^a   Verbeke, Geert ^a   Kenward, Mike ^b  

^a UNIVERSITY OF LEUVEN   (Belgium)

^b LONDON SCHOOL OF HYGIENE AND TROPICAL MEDICINE   (United Kingdom)

Author keywords

Linear mixed model; Longitudinal data

Indexed keywords

RANDOM PROCESSES;

CORRELATION COMPONENTS; COVARIANCE MATRICES; FLEXIBLE MODEL; LINEAR MIXED MODELS; LONGITUDINAL DATA; PARAMETRIC MODELS; RANDOM EFFECTS; RANDOM EFFECTS MODEL; SERIAL CORRELATION; WEIGHT GAIN;

COVARIANCE MATRIX;

EID: 0034359814     PISSN: 03233847     EISSN: None     Source Type: Journal    
DOI: 10.1002/1521-4036(200011)42:7<807::AID-BIMJ807>3.0.CO;2-3     Document Type: Article

References (23)

1. DAVIDIAN, M. and GILTANEN, D. M., 1995: Nonlinear Models for Repeated Measurement Data. Chapman and Hall, London.
2. DAWSON, K. S., GENNINGS, C., and CARTER, W. H., 1997: Two Graphical Techniques Useful in Detecting Correlation Structure in Repeated Measures Data. The American Statistician 51, 275-283.
3. DIGGLE, P., 1988: An Approach to the Analysis of Repeated Measures. Biometrics 44, 959-971.
4. DIGGLE, P., LIANG, K.-Y., and ZEGER, S. L., 1994: Analysis of Longitudinal Data. Oxford Science Publications, Oxford.
5. DIGGLE, P. J., TAWN, J. A., and MOYEED, R. A., 1998: Model-Based Geostatistics. Applied Statistics 47, 299-350.
6. DIGGLE, P. J. and VERBYLA, A. P., 1998: Nonparametric Estimation of Covariance Structure in Longitudinal Data. Biometrics 54, 401-415.
7. GILMOUR, A. R., THOMPSON, R. and CULLIS, B. R., 1998: ASREML. Biometric Bulletin: New South Wales Agriculture.
8. GOLDSTEIN, H., HEALY, M. J. R., and RASBACH, J., 1994: Multilevel Time Series Models with Applications to Repeated Measures Data. Statistics in Medicine 13, 1643-1655.
9. LAIRD, N. M. and WARE, J. H., 1982: Random-Effects Models for Longitudinal Data. Biometrics 38, 963-974.
10. LONGFORD, N. T., 1993: Random Coefficient Models, Oxford Science.
11. LESAFFRE, E., ASEFA, M., and VERBEKE, G., 1999: Assessing the Goodness-of-Fit of the Laird & Ware Model. An Example: The Jimma infant Survival Differential Longitudinal Study. Statistics in Medicine 18, 835-854.
12. LESAFFRE, E., VERBEKE, G., and KENWARD, M., 2000: On the Two-Stage interpretation of the Linear Mixed Effects Model, (in preparation).
13. MORRELL, C. H., PEARSON, J. D. and BRANT, L. J., 1997: Linear Transformations of Linear Mixed-Effects Models. The American Statistician 51, 338-343.
14. ROYSTON, P. and ALTMAN, D. G., 1994: Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Modelling (with Discussion). Applied Statistics 43, 429-467.
15. SAS institute inc. SAS Technical Report, SAS/STAT Software: Changes and Enhancements, through Release 6.11, Cary NC, SAS institute inc., Cary, North Carolina, 1996.
16. SMITH, D. M., ROBERTSON, W. H., and DIGGLE, P. J., 1996: Oswald: Object-Oriented Software for the Analysis of Longitudinal Data in S. Technical Report MA 96/192, Department of Mathematics and Statistics, University of Lancaster, LA14YF, U.K.
17. SPLUS 4.5,1998: Guide to Statistics, Mathsoft inc.
18. STRAM, D. A. and LEE, J. W., 1994: Variance Components Testing in the Longitudinal Mixed Effects Model. Biometrics 50, 1171-1177.
19. STRAM, D. A. and LEE, J. W., 1995: Correction To: "Variance Components Testing in the Longitudinal Mixed Effects Model". Biometrics 51, 1196.
20. VERBEKE, G. and MOLENBERGHS, G., 2000: Linear Mixed Models in Practice. Springer-Verlag in Statistics.
21. VERBEKE, G., LESAFFRE, E., and BRANT, L., 1998: The Detection of Residual Serial Correlation in Linear Mixed Models. Statistics in Medicine 17, 1391-1402.
22. VERBEKE, G., LESAFFRE, E., and SPIESSENS, B., 1998: The Practical Use of Different Strategies for Handling Dropout in Longitudinal Studies. (submitted).
23. VERBYLA, A. P., CULLIS, B. R., KENWARD, M. G., and WELHAM, S. J., 1999: The Analysis of Designed Experiments and Longitudinal Data Using Smoothing Splines (with Discussion). Applied Statistics 48, 269-311.