Analysis of repeated measures data in nutrition research
- Publication Type:
- Journal Article
- Citation:
- Frontiers in Bioscience - Landmark, 2019, 24 (7), pp. 1377 - 1389
- Issue Date:
- 2019-06-01
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| Filename | Description | Size | |||
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| getfile.php.pdf | Accepted Manuscript Version | 1.02 MB |
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© 2019 Frontiers in Bioscience. All rights reserved. Amino acid nutrition studies often involve repeated measures data. An example is that the concentrations of plasma citrulline in steers are repeatedly measured from the same animals. The standard repeated measures ANOVA method does not detect significant time changes in the concentrations of plasma citrulline within 6 hours after steers consumed rumen-protected citrulline, while a graphical analysis indicates that there exists a time effect. Here we describe three mixed model analyses that capture the time effect in a statistically significant way, while accounting for the correlations of measurements over time from the same steers. First, we allow flexible variance-covariance structures on our model. Second, we use baseline measurements as a covariate in our model. Third, we use percent-change from baseline as a data normalization method. In our data analysis, all these three approaches can lead to meaningful statistical results that oral administration of rumen-protected citrulline enhances the concentrations of plasma citrulline over time in ruminants. This supports the notion that rumen-protected citrulline can bypass the rumen to effectively enter the blood circulation.
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