Background: Cluster randomized trials (CRTs) are useful in practice-based research network translational research. However, simple or stratified randomization often yields study groups that differ on key baseline variables when the number of clusters is small. Unbalanced study arms constitute a potentially serious methodological problem for CRTs.
Methods: Covariate constrained randomization with data on relevant variables before randomization was used to achieve balanced study arms in 2 pragmatic CRTs. In study 1, 16 counties in Colorado were randomized to practice-based or population-based reminder recall for vaccinating children ages 19 to 35 months. In study 2, 18 primary care practices were randomized to computer decision support plus practice facilitation versus computer decision support alone to improve care for patients with stage 3 and 4 chronic kidney disease. For each study, a set of optimal randomizations, which minimized differences of key variables between study arms, was identified from the set of all possible randomizations.
Results: Differences between study arms were smaller in the optimal versus remaining randomizations. Even for the randomization in the optimal set with the largest difference between groups, study arms did not differ significantly on any variable for either study (P > .05).
Conclusions: Covariate constrained randomization, which restricts the full randomization set to a subset in which differences between study arms are minimized, is a useful tool for achieving balanced study arms in CRTs. Because of the increasing recognition of the risk of imbalance in CRTs and implications for interpreting study findings, procedures of this type should be considered in designing practice-based or community-based trials.
Keywords: Cluster Randomized Trials; Covariate-Based Constrained Randomization; Practice-based Research.
© Copyright 2015 by the American Board of Family Medicine.