Evaluating performance of covariate-constrained randomization (CCR) techniques under misspecification of cluster-level variables in cluster-randomized trials

Covariate constrained randomization (CCR) is a method of controlling imbalance in important baseline covariates in cluster-randomized trials (CRT). We use simulated CRTs to investigate the performance (control of imbalance) of CCR relative to simple randomization (SR) under conditions of misspecification of the cluster-level variable used in the CCR algorithm. We use data from a Patient-Centered Outcomes Research Institute (PCORI)-funded CRT evaluating the Mothers and Babies (MB) intervention (AD-1507-31,473). CCR methodology was used in the MB study to control imbalance in, among other baseline variables, the percent minority (i.e., non-White) participants at each study site. Simulation schemes explored variation in degree of misspecification in the baseline covariate of interest, and include correct report, observed misspecification, and a range of simulated misspecification for intervals within and beyond that observed in the MB study. We also consider three within-site sample size scenarios: that observed in the MB study, small (mean 10) and large (mean 50). Simulations at every level of baseline covariate misspecification suggest that use of the CCR strategy provides between-arm imbalance that is simultaneously lower and less variable, on average, than that produced from the SR strategy. We find that the gains to using CCR over SR are nearly twice as high with accurate reporting (Δ = −5.33) compared to the observed study-level misspecification (Δ = −3.03). Although CCR still outperforms SR as the level of misspecification increases, the gains to using CCR over SR decrease; thus, every effort should still be made to obtain high-quality baseline data.