Clinical trials and practice PUBLISHERS OpenJournal ISSN 2771-7380 Design and Statistical Methods for Handling Covariates Imbalance in Randomized Controlled Clinical Trials: Dilemmas Resolved Bolaji E. Egbewale, PhD * Department of Community Medicine, College of Health Sciences, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria * Corresponding author Bolaji E. Egbewale, PhD Associate Professor of Biostatistics, Department of Community Medicine, College of Health Sciences, Ladoke Akintola University of Technology, Ogbomoso, Oyo State, Nigeria; E-mail: beegbewale@lautech.edu.ng Article information Received: October 1 st , 2021; Revised: October 28 th , 2021; Accepted: November 5 th , 2021; Published: November 15 th , 2021 Cite this article Egbewale BE. Design and statistical methods for handling covariates imbalance in randomized controlled clinical trials: Dilemmas resolved. Clin Trial Pract Open J. 2021; 4(1): 22-29. doi: 10.17140/CTPOJ-4-121 Original Research Original Research | Volume 4 | Number 1 | 22 Copyright 2021 by Egbewale BE. This is an open-access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0), which allows to copy, redistribute, remix, transform, and reproduce in any medium or format, even commercially, provided the original work is properly cited. cc INTRODUCTION T he essence of randomization exercise is to bring about compa- rable treatment groups in a controlled trial. Treatment groups are expected to be similar in factors - known or unknown, that are prognostic of outcome of interest for investigator to draw a valid inference on treatment effect. 1 However, in practice, balance in covariates between groups is often not attained with randomiza- tion. 1,2 The resultant imbalance subtly opens the trial intervention to a degree of misrepresentation of estimates of effect. 3 The need for a correct and more reliable inference on the effect of interven- tions under trial has led to efforts to ensure that balance is achieved in the distribution of covariates between treatment groups. Po- tentially great studies at onset had ended up been declared incon- clusive owing to issues related to improper design, in particular, imbalance in risk factors between treatment groups. For example, in their study, Rosenberger et al 4 recall an abrupt termination of a trial on the role of erythropoietin in maintaining normal hemo- globin concentrations in patients with metastatic cancer. The trial which was supposed to be a major study involving 139 clinical sites ABSTRACT Introduction 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 identifed potential limitations associated with design and statistical approach- es 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 Stratifcation breaks down in small sample size trials and may not accommodate more than two stratifcation 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 stratifcation 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. Keywords Randomization; Covariate imbalance; Stratifcation; Minimization; Change score analysis; ANCOVA.