Title

Correlation Coefficients for a Study with Repeated Measures

Editors

Rafik Karaman

Document Type

Article

Publication Date

3-26-2020

Publication Title

Computational and Mathematical Methods in Medicine

Volume

2020

First page number:

1

Last page number:

11

Abstract

Repeated measures are increasingly collected in a study to investigate the trajectory of measures over time. One of the first research questions is to determine the correlation between two measures. The following five methods for correlation calculation are compared: (1) Pearson correlation; (2) correlation of subject means; (3) partial correlation for subject effect; (4) partial correlation for visit effect; and (5) a mixed model approach. Pearson correlation coefficient is traditionally used in a cross-sectional study. Pearson correlation is close to the correlations computed from mixed-effects models that consider the correlation structure, but Pearson correlation may not be theoretically appropriate in a repeated-measure study as it ignores the correlation of the outcomes from multiple visits within the same subject. We compare these methods with regard to the average of correlation and the mean squared error. In general, correlation under the mixed-effects model with the compound symmetric structure is recommended as its correlation is close to the nominal level with small mean square error.

Disciplines

Applied Mathematics

Language

English

UNLV article access

Search your library

Share

COinS