![cluster standard errors stata cluster standard errors stata](https://i.stack.imgur.com/OgZFF.jpg)
In the case of standard CRTs, there is a considerable literature dealing with more complicated scenarios, for example, when repeated measures are obtained from individuals within the clusters.
#Cluster standard errors stata trial#
In practice, a preliminary size is computed as if the trial were an individual RCT and the sample size is obtained by multiplying this by the DE, which thus quantifies the inflation in the sample size resulting from the reduced amount of information due to the lack of independence across the observations. In the simplest case, the DE is computed as a function of the number of individuals in each cluster and the intracluster correlation (ICC), which quantifies the proportion of the total variance due to variation between the clusters. The most common approach to computing the optimal sample size for a CRT is to formally include some form of variance inflation, often expressed in terms of a design effect (DE), the factor by which the sample size obtained for an individual RCT needs to be inflated to account for correlation in the outcome.
![cluster standard errors stata cluster standard errors stata](https://www.stata.com/features/i/db-tabulate2.png)
In the case of cluster RCTs (CRTs), where clusters rather than individuals are randomised, the outcomes for participants within a cluster are likely to be more similar than those between clusters. Sample size calculations for a trial are typically based on analytical formulae, often relying on the assumption of (approximate) normality of some test statistic used for the analysis.