Cluster randomized trial analysis made easy: The clan Stata command

It is well established that the analysis of cluster randomized trials must account for the correlation between observations in the same cluster. These trials randomize whole groups of individuals like hospitals or villages to receive either a control condition or intervention condition, and individuals in the same cluster are likely to be more similar to one another than individuals in different clusters. While accounting for correlation between observations can be done using regression-based methods such as mixed-effects models, these are known to perform less well with fewer than around 30 clusters, which is common for cluster randomized trials. The cluster-level analysis, where the data are summarized for each cluster and then the cluster summaries analyzed as independent data points, is well established but much less commonly used in practice. In this talk, I will present some of the benefits and drawbacks of using the cluster-level analysis method and introduce the clan Stata command, which simplifies implementation of this approach.