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Activity Number: 544
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract #316831
Title: Modelling Nested Repeated Measures Designs Using Statistical Software
Author(s): Jeffrey Burton* and Robbie A. Beyl and William Johnson
Companies: Pennington Biomedical Research Center and Pennington Biomedical Research Center and Pennington Biomedical Research Center
Keywords: Nested design ; Repeated Measures ; Covariance Matrix ; Accuracy of p-values ; F-test
Abstract:

Here, we consider experimental designs employed in clinical research settings that incorporate multiple nested levels of repeated measures. Fitting covariance structures in mixed effects models to data with more than two levels of repeated measures becomes complicated when using standard statistical software. Collapsing the data to simplify the analysis, though requiring misspecification of the covariance matrix, is investigated as an alternative. Using different methods of collapsing the data, null properties of F-tests constructed under models with different covariance structures specified are investigated. The results indicate that collapsing the data and, thus, misspecifying the covariance matrix produces mixed results seemingly dependent on the method of collapsing and the true underlying values and structure of variance components.


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