XTGEEBCV: Stata module to compute bias-corrected (small-sample) standard errors for generalized estimating equations

Cluster randomized trials (CRTs), where clusters (e.g., schools or clinics) are randomized to comparison arms but measurements are taken on individuals, are commonly used to evaluate interventions in public health, education, and the social sciences. Analysis is often conducted on individual-level outcomes, and such analysis methods must take into account that outcomes for members of the same cluster tend to be more similar than those for members of other clusters. A popular individual-level analysis technique is generalized estimating equations (GEE). However, it is common to randomize a small number of clusters (e.g., 30 or fewer), and in this case, the GEE standard errors obtained from the sandwich variance estimator will be biased, leading to inflated type I errors. A number of bias-corrected standard errors have been proposed and studied to account for this finite-sample bias, yet most have not yet been implemented in Stata. Our newly-created Stata program xtgeebcv allows users to easily implement these bias corrections in the GEE framework.