Allocation techniques for balance at baseline in cluster randomized trials: a methodological review

Abstract

Reviews have repeatedly noted important methodological issues in the conduct and reporting of cluster randomized controlled trials (C-RCTs). These reviews usually focus on whether the intracluster correlation was explicitly considered in the design and analysis of the C-RCT. However, another important aspect requiring special attention in C-RCTs is the risk for imbalance of covariates at baseline. Imbalance of important covariates at baseline decreases statistical power and precision of the results. Imbalance also reduces face validity and credibility of the trial results. The risk of imbalance is elevated in C-RCTs compared to trials randomizing individuals because of the difficulties in recruiting clusters and the nested nature of correlated patient-level data. A variety of restricted randomization methods have been proposed as way to minimize risk of imbalance. However, there is little guidance regarding how to best restrict randomization for any given C-RCT. The advantages and limitations of different allocation techniques, including stratification, matching, minimization, and covariate-constrained randomization are reviewed as they pertain to C-RCTs to provide investigators with guidance for choosing the best allocation technique for their trial.

Evidence-based vaccination strategies in obstetrics and gynecology settings: Current practices and methods for assessment

“…The most common methods for achieving balance, stratification and matching, are often insufficient in achieving balance. 14,21 In this current study, we demonstrate a method for achieving such balance on important covariates that could affect the study's outcome. To do this, it is important to note that investigators may need to spend significant time collecting data on the chosen covariates before randomization takes place.…”

A Community Engagement Method to Design Patient Engagement Materials for Cardiovascular Health

“…Randomization methods that improve study arm balance in terms of underlying populations and regional differences are essential for community-based trials. 19 We describe (1) the results of 4 BCTs in unique communities across diverse regions of Colorado and New Mexico and (2) approaches to study design challenges for a randomized trial evaluating the effect of patient, family, and community engagement on practice transformation and cardiovascular risk reduction. …”

Pragmatic Cluster Randomized Trials Using Covariate Constrained Randomization: A Method for Practice-based Research Networks (PBRNs)

“…When there are relatively few clusters with diverse settings and populations, however, simple randomization can result in study arms that differ substantially on key process and clinical variables or potential confounders, such as sociodemographic or contextual features. [5][6][7][8][9] Imbalance in a trial weakens the case for causal inference, the key strength of a randomized controlled trial, because observed differences between study arms following an intervention may not be attributable to the intervention, but rather may be the result of differences in the underlying study populations. 5 In addition, imbalance can result in differences between crude and adjusted estimates of treatment effects, thus hindering the interpretability and face validity of the findings.…”

“…[5][6][7][8][9] Imbalance in a trial weakens the case for causal inference, the key strength of a randomized controlled trial, because observed differences between study arms following an intervention may not be attributable to the intervention, but rather may be the result of differences in the underlying study populations. 5 In addition, imbalance can result in differences between crude and adjusted estimates of treatment effects, thus hindering the interpretability and face validity of the findings. 5,10 -12 This problem has resulted in questions regarding the validity and/or generalizability of results from CRTs.…”

“…Stratification (block randomization) and matching have most commonly been used, but these are feasible for only a limited number of categorical variables 16 and may be ineffective in achieving balance. 5,11 In addition, stratifying or matching on combinations of multiple variables is often not even possible. Covariate-constrained randomization, 17-23 a more complex but particularly promising approach, is feasible when the number of variables is larger or includes continuous variables.…”