Cluster Randomized Trials: Design and Analysis

Abstract

The first section of this chapter gives an introduction to cluster randomized trials, and the reasons why such trials are often chosen above simple randomized trials. It also argues that more advanced statistical methods for data obtained from such trials are required, since these data are correlated due to the nesting of persons within clusters. Traditional statistical techniques, such as the regression model ignore this dependency, and thereby result in incorrect conclusions with respect to the effect of treatment. In the first section it is also argued that the design of cluster randomized trials is more complicated than that of simple randomized trials; not only the total sample size needs to be determined, but also the number of clusters and the number of persons per cluster. The second section describes and compares the multilevel regression model and the mixed effects analysis of variance (ANOVA) model. These models explicitly take into account the nesting of persons within clusters, and thereby the dependency of outcomes of persons within the same cluster. It is shown that the traditional regression model leads to an inflated type I error rate for treatment testing. Optimal sample sizes for cluster randomized trials are given in Sects. 39.3 and 39.4. These sample sizes can be shown to depend on the intra-class correlation coefficient, which measures the amount of variance in the outcome variable at the cluster level. A guess of the true value of this parameter must be available in the design stage in order to calculate the optimal sample sizes. Section 39.5 focuses on the robustness of the optimal sample size against incorrect guesses of this parameter. Section 39.6 focuses on optimal designs when the aim is to estimate the intra-class correlation with the greatest precision.