Balance algorithm for cluster randomized trials
Balance algorithm for cluster randomized trials
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DOI：
10.1186/1471-2288-8-65
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发表时间：
2008-10-09
影响因子：
4
通讯作者：
Hood, Kerenza
中科院分区：
文献类型：
--
作者：
Carter, Ben R.;Hood, Kerenza
背景资料：在整群随机化试验中，不存在生成区组随机化的完整枚举、平衡治疗组间协变量的算法。此外，由于实际原因，通常需要多个区组才能完全随机化研究，而区组内可能没有很好地平衡。结果：我们提出了一种方便且易于使用的随机化工具来进行分配隐藏区组随机化。我们的算法突出了分配，最大限度地减少治疗组之间的不平衡，在多个基线covariates.We证明了算法使用的群集随机试验在初级保健（PRE-EMPT研究），并表明，该软件采用了独立的随机分配之间的权衡，可能是不平衡的，可预测的确定性方法，将最大限度地减少不平衡。我们扩展的方法，单块随机分配到多个区块conditioning上以前allocations.Conclusion：该算法包括作为附加文件I，我们主张其用于集群随机试验内的强大的随机化。
Background: 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.Results: 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.Conclusion: The algorithm is included as Additional file I and we advocate its use for robust randomization within cluster randomized trials.