Comparison of dynamic block randomization and minimization in randomized trials: a simulation study

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).

Behavioral Weight Loss and Physical Activity Intervention in Obese Adults with Asthma. A Randomized Trial

“…We applied our published dynamic block randomization method (14) to assure better than chance between-treatment balance across six prognostic factors (study site, age, sex, race/ethnicity, BMI, and ACQ score). The method automatically ensures allocation concealment.…”

Evaluation of a culturally-adapted lifestyle intervention to treat elevated cardiometabolic risk of Latino adults in primary care (Vida Sana): A randomized controlled trial

“…Randomization and Blinding-Eligible participants will be randomized in a 1:1 ratio to receive usual care or usual care plus the intervention (n = 93/arm). We will apply a covariate-adaptive biased coin method that we have published 53 and used successfully in several trials 54 , 55 to achieve good marginal balance between treatments across the following baseline characteristics: clinic, age, sex, BMI, waist circumference, and level of acculturation assessed by the Short Acculturation Scale for Hispanics. 56 , 57 The dynamic block randomization algorithm of our method automatically ensures allocation concealment.…”

The Performance of Randomization Methods in Consideration of Prognostic Factors for Small-Size Clinical Trials: A Simulation Study

“…There have been several researches that evaluated the performance of these methods (Therneau, 1993;Weir and Lees, 2003;Hagino et al, 2004;Brown et al, 2005;Toorawa et al, 2009;Xiao et al, 2011;Kahan and Morris, 2013). The performance of randomization methods may be related to trial sample size, but there are only a few studies of this focused on small-size clinical trials.…”