Abstract
Multilevel random-effects models have become a popular method in the analysis of clustered data. Such analyses enable researchers to quantify within-cluster and between-cluster variations of an outcome and to separate individual-level and cluster-level effects of covariates by taking advantage of the hierarchical structure of clustered data. The tutorial article by Austin and Merlo(1) was a timely effort intended to provide a comprehensive and up-to-date review of the tools and approaches. However, we feel that some important ideas and concepts described in this article need clarification.