The analysis of change is central to the study of kidney research. In the past 25 years, newer and more sophisticated methods for the analysis of change have been developed; however, as of yet these newer methods are underutilized in the field of kidney research. Repeated measures ANOVA is the traditional model that is easy to understand and simpler to interpret, but it may not be valid in complex real-world situations. Problems with the assumption of sphericity, unit of analysis, lack of consideration for different types of change, and missing data, in the repeated measures ANOVA context are often encountered. Multilevel modeling, a newer and more sophisticated method for the analysis of change, overcomes these limitations and provides a better framework for understanding the true nature of change. The present article provides a primer on the use of multilevel modeling to study change. An example from a clinical study is detailed and the method for implementation in SAS is provided.

Keywords:
Longitudinal data analysis, Analysis of change, Change over time, Repeated measures, Multilevel modeling, Mixed effects models, Random coefficient models, Hierarchical linear models, Unit of analysis
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© 2008 S. Karger AG, Basel
2008
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