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Abstract:
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RCTs balance all covariates on average and provide the gold standard for estimating treatment effects. Chance imbalances however exist more or less in realized treatment allocations, subjecting subsequent inference to possibly large variability. Modern scientific publications require the reporting of covariate balance tables with not only covariate means by treatment group but also the associated p-values from significance tests of their differences. The practical need to avoid small p-values renders balance check and rerandomization by hypothesis testing an attractive tool for improving covariate balance in RCTs. We examine a variety of potentially useful schemes for rerandomization based on p-values (ReP) from covariate balance tests, and demonstrate their impact on subsequent inference. The main findings are twofold. First, the estimator from the fully interacted regression is asymptotically the most efficient under all ReP schemes examined, and permits convenient regression-assisted inference identical to that under complete randomization. Second, ReP improves not only covariate balance but also the efficiency of the estimators from the unadjusted and additive regressions.
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