Carter Ben R, Hood Kerenza
South East Wales Trials Unit, Neuadd Meirionnydd, School of Medicine, Heath Park Campus, Cardiff University, CF14 4XN, UK.
BMC Med Res Methodol. 2008 Oct 9;8:65. doi: 10.1186/1471-2288-8-65.
Within cluster randomized trials no algorithms exist to generate a full enumeration of a block randomization, balancing for covariates across treatment arms. Furthermore, often for practical reasons multiple blocks are required to fully randomize a study, which may not have been well balanced within blocks.
We present a convenient and easy to use randomization tool to undertake allocation concealed block randomization. Our algorithm highlights allocations that minimize imbalance between treatment groups across multiple baseline covariates. We demonstrate the algorithm using a cluster randomized trial in primary care (the PRE-EMPT Study) and show that the software incorporates a trade off between independent random allocations that were likely to be imbalanced, and predictable deterministic approaches that would minimise imbalance. We extend the methodology of single block randomization to allocate to multiple blocks conditioning on previous allocations.
The algorithm is included as Additional file 1 and we advocate its use for robust randomization within cluster randomized trials.
在整群随机试验中,不存在用于生成完全列举的区组随机化的算法,无法在各治疗组间平衡协变量。此外,出于实际原因,通常需要多个区组才能使一项研究完全随机化,而这些区组内部可能并未实现良好的平衡。
我们提出了一种方便易用的随机化工具,用于进行分配隐藏的区组随机化。我们的算法突出显示了能使多个基线协变量在治疗组间失衡最小化的分配方式。我们在一项初级保健的整群随机试验(PRE - EMPT研究)中演示了该算法,并表明该软件在可能失衡的独立随机分配与能使失衡最小化的可预测确定性方法之间进行了权衡。我们扩展了单区组随机化方法,以便根据先前的分配情况分配到多个区组。
该算法作为补充文件1包含在内,我们提倡在整群随机试验中使用它进行稳健的随机化。