Effects of covariance model assumptions on hypothesis tests for repeated measurements: analysis of ovarian hormone data and pituitary-pteryomaxillary distance data
Article first published online: 10 AUG 2001
Copyright © 2001 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 20, Issue 16, pages 2441–2453, 30 August 2001
How to Cite
Park, T., Park, J.-K. and Davis, C. S. (2001), Effects of covariance model assumptions on hypothesis tests for repeated measurements: analysis of ovarian hormone data and pituitary-pteryomaxillary distance data. Statist. Med., 20: 2441–2453. doi: 10.1002/sim.859
- Issue published online: 10 AUG 2001
- Article first published online: 10 AUG 2001
- Korea Science and Engineering Foundation. Grant Number: 1999-1-104-001-5
- Brain Korean 21 Project
In the analysis of repeated measurements, multivariate methods which account for the correlations among the observations from the same experimental unit are widely used. Two commonly-used multivariate methods are the unstructured multivariate approach and the mixed model approach. The unstructured multivariate approach uses MANOVA types of models and does not require assumptions on the covariance structure. The mixed model approach uses multivariate linear models with random effects and requires covariance structure assumptions. In this paper, we describe the characteristics of tests based on these two methods of analysis and investigate the performance of these tests. We focus particularly on tests for group effects and parallelism of response profiles. Copyright © 2001 John Wiley & Sons, Ltd.