Using cluster robust standard errors when analyzing cluster randomized trials with few clusters

FL Huang, Xintong Li

Abstract

Accounting for dependent observations in cluster randomized trials (CRTs) using nested data is necessary in order to avoid misestimated standard errors resulting in questionable inferential statistics. Cluster robust standard errors (CRSEs) are often used to address this issue. However, CRSEs are still well-known to underestimate standard errors for group-level variables when the number of clusters is low (e.g., < 50) and with CRTs, a small number of clusters, due to logistical or financial considerations, is the norm rather than the exception. Using a simulation with various conditions, we investigate the use of a small sample correction (i.e., CR2 estimator) proposed by Bell and McCaffrey (2002) together with empirically-derived degrees of freedom estimates (dofBM). Findings indicate that even with as few as 10 clusters, the CR2 estimator used with dofBM, yields generally unbiased results with acceptable Type I error and coverage rates. Results show that coverage and Type I error rates can be largely influenced by the choice of dof, not just the standard error adjustments. An applied example is provided together with R syntax to conduct the analysis. To facilitate the use of different CRSEs, a free graphical, menu-driven SPSS add-on to compute the various cluster robust variance estimates can be downloaded from http://faculty.missouri.edu/huangf/CR2/Cluster_Robust_Reg.spd.