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Report, 2004

Modelling Mean-Covariance Structures in the Growth Curve Models

Pan , von Rosen Dietrich


The growth curve model (GCM) has been widely used in longitudinal studies and repeated measures. Most existing spproaches for statistical inference in the GCM assume a specific structure on the within-subject covariances e.g., compound symmetry, AR(1) and unstructured covariances. This specification, however, may select a suboptimal or even wrong model, which in turn may affect the estimates of regression co-efficients and/or bias standard errors of the estimates. Accordingly, statistical inferences of the GCM may be severely affected by misspecification of covariance structures. Within the framework of the GCM in this paper we propose a data-driven approach for modelling the within-subject covariance structures, investigate the effects of misspecification of covariance structures on statistical inferences and study the possible heterogeneity of covariances between different treatment groups


Covariance structures; growth curve models; heterogeneity of covariances; joint mean-covariance modelling; maximum likelihood estimation; misspecification of covariance structures

Published in

Research report (Centre of Biostochastics)
2004, number: 4
Publisher: Biostochasticum

    SLU Authors

    UKÄ Subject classification

    Animal and Dairy Science
    Agricultural Science
    Landscape Architecture
    Environmental Sciences related to Agriculture and Land-use
    Fish and Aquacultural Science
    Renewable Bioenergy Research
    Veterinary Science
    Social Sciences
    Forest Science
    Economics and Business
    Food Science

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