Design and Statistical Methods for Handling Covariates Imbalance in Randomized Controlled Clinical Trials: Dilemmas Resolved

In practice, between groups baseline imbalance following randomization not only opens effect estimate to bias in controlled trials, it also has certain ethical consequences. Both design and statistical approaches to ensure balanced treatment groups in prognostic factors are not without their drawbacks. This article identified potential limitations associated with design and statistical approaches for handling covariate imbalance in randomized controlled clinical trials (RCTs) and proffered solutions to them. Methods A careful review of literatures coupled with a robust appraisal of statistical models of methods involved in a way that compared their strength and weaknesses in trial environments, was adopted. Results Stratification breaks down in small sample size trials and may not accommodate more than two stratification factors in practice. On the other hand, minimization that balances for multiple prognostic factors even in small trials is not a pure random procedure and in addition, could present with complexities in computations. Overall, either minimization or stratification factors should be included in the model for statistical adjustment. Statistically, estimate of effect by change score analysis (CSA) is susceptible to direction and magnitude of imbalance. Only analysis of covariance (ANCOVA) yields unbiased effect estimate in all trial scenarios including situations with baseline imbalance in known and unknown prognostic covariates. Conclusion Design methods for balancing covariates between groups are not without their limitations. Both direction and size of baseline imbalance also have profound consequence on effect estimate by CSA. Only ANCOVA yields unbiased treatment effect and is recommended at all trial scenarios, whether or not between groups covariate imbalance matters.