Quantile dispersion graphs to compare the efficiencies of cluster randomized designs

Abstract

The purpose of this article is to compare efficiencies of several cluster randomized designs using the method of quantile dispersion graphs (QDGs). A cluster randomized design is considered whenever subjects are randomized at a group level but analyzed at the individual level. A prior knowledge of the correlation existing between subjects within the same cluster is necessary to design these cluster randomized trials. Using the QDG approach, we are able to compare several cluster randomized designs without requiring any information on the intracluster correlation. For a given design, several quantiles of the power function, which are directly related to the effect size, are obtained for several effect sizes. The quantiles depend on the intracluster correlation present in the model. The dispersion of these quantiles over the space of the unknown intracluster correlation is determined, and then depicted by the QDGs. Two applications of the proposed methodology are presented.quantile dispersion graphs, power function, intracluster correlation, effect size, noncentrality parameter,

Methods for sample size determination in cluster randomized trials

“…This occurred in a trial of mental illness because the outcome measure was a newly adaptive questionnaire with unknown properties. 48 In these situations, several approaches might be considered: an educated estimate could be gained from assessment of published ICCs and known patterns in their behaviour for different outcome types and clusters; graphical methods that compare competing designs without requiring knowledge of the ICC 49 ; or an internal pilot could be considered (see later section).…”