A simple approach to test for interaction between intervention and an individual-level variable in community randomized trials
A simple approach to test for interaction between intervention and an individual-level variable in community randomized trials
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DOI：
10.1111/j.1365-3156.2007.01997.x
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发表时间：
2008-02-01
影响因子：
3.3
通讯作者：
Milligan, Paul
中科院分区：
文献类型：
--
作者：
Cheung, Yin Bun;Jeffries, David;Milligan, Paul
目的建立一种简单、可靠的方法，用于检验社区干预与个体水平变量之间的相互作用，适用于社区随机试验（CRTs）的典型情况。即社区数量少，但每个社区有大量的受试者。方法我们提出了一种方法，该方法基于从每个属性内有和没有属性的个体组中获取汇总统计量之间的差异社区，然后应用双样本t检验或Wilcoxon检验来比较试验组之间社区内差异的分布。该方法进行了评估，使用模拟和说明使用CRT的健康教育干预的数据。近似的样本量formulsderived.RESULTS分析的基础上的t-检验的功率非常接近预期的水平，在各种情况下，包括当汇总统计量不对称分布在社区，协变量不按计划分布，每个干预臂的社区数量范围从8到20。即使在每个分支只有四个社区的情况下，电力也只是略低于预期。无论分布假设是否正确，第一类错误率总是严格遵循5%的要求。Wilcoxon检验的应用似乎过于保守。结论所提出的方法来测试的相互作用是有效的，易于使用。t检验在这种情况下的应用具有稳健性。
OBJECTIVE To develop a simple and robust approach for the test of interaction between community intervention and an individual-level variable suitable for use in typical situations of community randomized trials (CRTs), i.e. small number of communities but large number of subjects per community.METHODS We propose a method based on taking the difference between summary statistics from groups of individuals with and without an attribute within each community, then applying a two-sample t-test or Wilcoxon test to compare the distribution of within-community differences between trial arms. The method is evaluated using simulations and illustrated using data from a CRT of a health education intervention. Approximate sample size formulas are derived.RESULTS Analyses based on the t-test give power very close to expected level in a variety of situations, including when the summary statistics are not symmetrically distributed across communities, the covariate is not distributed as planned, and the number of communities per intervention arm ranges from 8 to 20. Even in the situation with as few as four communities per arm, the power is only slightly lower than expected. Type I error rates always closely follow 5% as required, whether the distributional assumption is correct or not. The application of the Wilcoxon test appears too conservative.CONCLUSIONS The proposed approach to test for interaction is valid and easy to use. The application of the t-test in this setting is robust.