Randomised assignment of individuals to treatment and controls groups is often considered the gold standard to draw valid conclusions about the efficacy of an intervention. In practice, randomisation can lead to accidental differences due to chance. Researchers have offered alternatives to reduce such differences, but these methods are not used frequently due to the requirement of advanced statistical methods. Here, we recommend a simple assignment procedure based on variance minimisation (VM), which assigns incoming participants automatically to the condition that minimises differences between groups in relevant measures. As an example of its application in the research context, we simulated an intervention study whereby a researcher used the VM procedure on a covariate to assign participants to a control and intervention group rather than controlling for the covariate at the analysis stage. Among other features of the simulated study, such as effect size and sample size, we manipulated the correlation between the matching covariate and the outcome variable and the presence of imbalance between groups in the covariate. Our results highlighted the advantages of VM over prevalent random assignment procedure in terms of reducing the Type I error rate and providing accurate estimates of the effect of the group on the outcome variable. The VM procedure is valuable in situations whereby the intervention to an individual begins before the recruitment of the entire sample size is completed. We provide an Excel spreadsheet, as well as scripts in R, MATLAB, and Python to ease and foster the implementation of the VM procedure.

将个体随机分配到治疗组和对照组通常被认为是就干预效果得出有效结论的金标准。在实践中，随机化可能会由于偶然性而导致意外差异。研究人员提供了减少此类差异的替代方法，但由于需要高级统计方法，这些方法并未经常使用。在这里，我们推荐一个基于方差最小化 (VM) 的简单分配程序，该程序自动将传入的参与者分配到最小化相关措施中组间差异的条件。作为其在研究环境中应用的一个例子，我们模拟了一项干预研究，研究人员在协变量上使用 VM 程序将参与者分配到对照组和干预组，而不是在分析阶段控制协变量。在模拟研究的其他特征（如效应量和样本量）中，我们操纵了匹配协变量与结果变量之间的相关性以及协变量中组间不平衡的存在。我们的结果强调了 VM 在降低 I 类错误率和准确估计组对结果变量的影响方面优于流行的随机分配程序的优势。VM 程序在对个体的干预在整个样本量的招募完成之前开始的情况下很有价值。我们提供 Excel 电子表格，