Abstract

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

Keywords

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

• von Rosen, Dietrich

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

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
Horticulture
Forest Science
Economics and Business
Food Science


Permanent link to this page (URI)

https://res.slu.se/id/publ/4521