Adaptive Randomization Method to Prevent Extreme Instances of Group Size and Covariate Imbalance in Stroke Trials

Abstract

BACKGROUND: A recent review of randomization methods used in large multicenter clinical trials within the National Institutes of Health Stroke Trials Network identified preservation of treatment allocation randomness, achievement of the desired group size balance between treatment groups, achievement of baseline covariate balance, and ease of implementation in practice as critical properties required for optimal randomization designs. Common-scale minimal sufficient balance (CS-MSB) adaptive randomization effectively controls for covariate imbalance between treatment groups while preserving allocation randomness but does not balance group sizes. This study extends the CS-MSB adaptive randomization method to achieve both group size and covariate balance while preserving allocation randomness in hyperacute stroke trials. METHODS: A full factorial in silico simulation study evaluated the performance of the proposed new CSSize-MSB adaptive randomization method in achieving group size balance, covariate balance, and allocation randomness compared with the original CS-MSB method. Data from 4 existing hyperacute stroke trials were used to investigate the performance of CSSize-MSB for a range of sample sizes and covariate numbers and types. A discrete-event simulation model created with AnyLogic was used to dynamically visualize the decision logic of the CSSize-MSB randomization process for communication with clinicians. RESULTS: The proposed new CSSize-MSB algorithm uniformly outperformed the CS-MSB algorithm in controlling for group size imbalance while maintaining comparable levels of covariate balance and allocation randomness in hyperacute stroke trials. This improvement was consistent across a distribution of simulated trials with varying levels of imbalance but was increasingly pronounced for trials with extreme cases of imbalance. The results were consistent across a range of trial data sets of different sizes and covariate numbers and types. CONCLUSIONS: The proposed adaptive CSSize-MSB algorithm successfully controls for group size imbalance in hyperacute stroke trials under various settings, and its logic can be readily explained to clinicians using dynamic visualization.