CVCRAND: Stata module for efficient design and analysis of Cluster Randomized Trials

Cluster randomized trials (CRTs), where clusters (for example, schools or clinics) are randomized but measurements are taken on individuals, are commonly used to evaluate interventions in public health and social science. Because CRTs typically involve only a few clusters, simple randomization frequently leads to baseline imbalance of cluster characteristics across treatment arms, threatening the internal validity of the trial. In CRTs with a small number of clusters, classic approaches to balancing baseline characteristics—such as matching and stratification—have several drawbacks, especially when the number of baseline characteristics the researcher desires to balance is large. An alternative approach is constrained randomization, whereby an allocation scheme is randomly selected from a subset of all possible allocation schemes based on the value of a balancing criterion. Subsequently, an adjusted clustered permutation test can be used in the analysis, which provides increased efficiency under constrained randomization compared with simple randomization. The program cvcrand facilitates the application of constrained randomization, and the program cptest allows the user to perform a clustered permutation test to analyze their cluster randomized trial.