An evaluation of constrained randomization for the design and analysis of group-randomized trials.
An evaluation of constrained randomization for the design and analysis of group-randomized trials.
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对群体随机试验的设计和分析的约束随机化评估。
DOI：
10.1002/sim.6813
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发表时间：
2016-05-10
影响因子：
2
通讯作者：
DeLong ER
中科院分区：
文献类型：
--
作者：
Li F;Lokhnygina Y;Murray DM;Heagerty PJ;DeLong ER
在组随机试验中，采用严格的研究设计的一个常见的实际限制是只有少数组可用，因此不能依靠简单的随机化来平衡各组间的关键组水平预后因素。约束随机化是为了保证平衡而提出的一种分配技术，可以与置换检验一起用于基于随机化的推理。然而，当考虑约束随机化时，一些统计问题尚未得到充分研究。因此，我们使用模拟来评估关键问题，包括：选择候选集大小和用于指导随机化的平衡度量的影响；调整与未调整分析的选择；以及基于模型和基于随机的测试的使用。我们进行了一项模拟研究，比较f检验和排列检验在群体水平潜在混杂因素存在下的I型误差和功率。我们的研究结果表明，相对于简单随机化，调整后的f检验和排列检验在约束随机化方面的表现相似，甚至略好于简单随机化，候选集的大小并没有实质上影响它们的功率。然而，在约束随机化条件下，未经调整的f检验是保守的，而未经调整的排列检验在候选集大小不太小的情况下具有期望的I型错误率；未经调整的排列检验始终比未经调整的f检验更有效，并且随着候选集大小的变化而增强。最后，我们警告在约束随机化下不适当的排列分布规范。一个正在进行的组随机试验被用作约束随机化设计的说明性例子。
In group-randomized trials, a frequent practical limitation to adopting rigorous research designs is that only a small number of groups may be available, and therefore simple randomization cannot be relied upon to balance key group-level prognostic factors across the comparison arms. Constrained randomization is an allocation technique proposed for ensuring balance, and can be used together with a permutation test for randomization-based inference. However, several statistical issues have not been thoroughly studied when constrained randomization is considered. Therefore, we used simulations to evaluate key issues including: the impact of the choice of the candidate set size and the balance metric used to guide randomization; the choice of adjusted versus unadjusted analysis; and the use of model-based versus randomization-based tests. We conducted a simulation study to compare the type I error and power of the F-test and the permutation test in the presence of group-level potential confounders. Our results indicate that the adjusted F-test and the permutation test perform similarly and slightly better for constrained randomization relative to simple randomization in terms of power, and the candidate set size does not substantially affect their power. Under constrained randomization, however, the unadjusted F-test is conservative while the unadjusted permutation test carries the desired type I error rate as long as the candidate set size is not too small; the unadjusted permutation test is consistently more powerful than the unadjusted F-test, and gains power as candidate set size changes. Finally, we caution against the inappropriate specification of permutation distribution under constrained randomization. An ongoing group-randomized trial is used as an illustrative example for the constrained randomization design.