Xiao L, Lavori PW, Wilson SR, Ma J., Clin Trials. 8(1):59-69. doi: 10.1177/1740774510391683., 2011 Feb 01
Investigators
Sandra Wilson, Ph.D., Senior Scientist
Abstract
BACKGROUND: Minimizing the imbalance of key baseline covariates between treatments is known to be very important to the precision of the estimate of treatment effect in clinical research. Dynamic randomization allocation techniques have been used to achieve balance across multiple baseline characteristics. However, empirical data are limited on how these techniques compare in terms of balance and efficiency. We are motivated by a newly funded randomized controlled trial, in which we have the option of choosing between two methods of randomization at the subject level: (1) randomizing individual subjects consecutively as they are enrolled, using Pocock and Simon's minimization method, and (2) simultaneously randomizing blocks of subjects once all subjects in a block have been enrolled, using a balance algorithm originally developed for cluster randomized trials.
PURPOSE: To compare dynamic block randomization and minimization in terms of balance on baseline covariates and statistical efficiency. Simple randomization was included as a reference.
METHODS: A simulation study using data from a previous randomized controlled trial was conducted to compare balance statistics and the accuracy and power of hypothesis testing among the randomization methods.
RESULTS: Dynamic block randomization consistently produced the best balance and highest power for various sample and treatment effect sizes, even after post-adjustment of the pre-specified baseline covariates in all three methods. Consistent with previous reports, minimization performed better in balance and power than simple randomization; however, the differences were noticeably smaller compared to those between dynamic block randomization and simple randomization.
LIMITATIONS: In this simulation study, we considered three sample sizes and two block sizes for a two-arm randomized trial. We assumed no interactions among the multiple baseline covariates. It is necessary to evaluate how the results may vary when the simulation conditions are changed before drawing broader conclusions regarding comparisons between the randomization methods.
CONCLUSIONS: This study demonstrates that dynamic block randomization outperforms minimization with regard to achieving balance and maximizing efficiency. Nevertheless, the differences across the three randomization strategies are modest. The statistical advantages associated with dynamic block randomization need to be considered in relation to the planned sample size and the practical issues for its implementation in deciding the preferred method of randomization for a given trial (e.g., the time required to accrue blocks of subjects of adequate size as balanced against the need to commence intervention/treatment immediately in those randomized to that experimental condition).