A comparison between traditional methods and multilevel regression for the analysis of multicenter intervention studies.

This article reviews three traditional methods for the analysis of multicenter trials with persons nested within clusters, i.e., centers, namely naïve regression (persons as units of analysis), fixed effects regression, and the use of summary measures (clusters as units of analysis), and compares these methods with multilevel regression. The comparison is made for continuous (quantitative) outcomes, and is based on the estimator of the treatment effect and its standard error, because these usually are of main interest in intervention studies. When the results of the experiment have to be valid for some larger population of centers, the centers in the intervention study have to present a random sample from this population and multilevel regression may be used. It is shown that the treatment effect and especially its standard error, are generally incorrectly estimated by the traditional methods, which should, therefore, not in general be used as an alternative to multilevel regression.