A simulation-based comparison of minimization, rerandomization, and anticlustering for creating experimental conditions Author Accepted Manuscript

Anticlustering has been used as a novel method to assign subjects to conditions in experiments. Anticlustering can be applied when covariate measurements are available at the beginning of an experiment and minimizes differences in covariates between conditions. In a simulation study implementing a two-group between-subjects design, we compared anticlustering with established methods for minimizing covariate imbalance: rerandomization and minimization. Anticlustering most strongly reduced covariate imbalance, followed by rerandomization and minimization. Lower covariate imbalance increased the precision of the effect size estimate. The average statistical power of the unadjusted analysis (independent t-test) was not improved when using covariate-based assignment as compared to random assignment. However, with random assignment, the statistical power of the unadjusted analysis depended on observed covariate imbalance; with covariate-based assignment, the statistical power of the unadjusted analysis was less affected by covariate imbalance because imbalance was minimized. Statistical adjustment via regression was most important to maximize statistical power.