Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials.
Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials.
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DOI：
10.1186/s12874-015-0026-x
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发表时间：
2015-04-23
影响因子：
4
通讯作者：
Redden DT
中科院分区：
文献类型：
--
作者：
Li P;Redden DT
在随机聚类试验(CRT)中，聚类个数少、聚类大小变化大是影响统计分析有效性和效率的关键因素。在广义线性混合模型(GLMM)中，常用F检验来检验CRT的干预效果。近似Wald F检验最具挑战性的问题是分母自由度(DDF)的估计。已经提出了一些DDF近似方法，但它们在分析非均相团簇较少的CRT中的二元结果时的小样本性能还没有得到很好的研究。在CRT框架下，通过仿真比较了五种DDF近似在F检验中的小样本性能。具体地说，我们说明了在GLMM中使用不同的DDF近似方法来测试具有二元结果的CRT的干预效果时，组内相关性(ICC)、样本大小和簇大小的变化如何影响I类误差和统计功率。使用真实的CRT数据集对结果进行了说明。我们的仿真结果表明，即使在簇总数低至10个的情况下，两个簇之间的方法仍能保持标称的第I类错误率，并且对簇大小的变化具有很强的鲁棒性。当簇数较小时，残差和遏制方法会增加I类错误率，并且随着簇大小的增加，膨胀会变得更加严重。相反，当总的簇数很小时，Satterthwaite和Kenward-Roger方法可以提供非常保守的I型错误率，并且随着簇大小的变化，保守性变得更加严重。我们的模拟还表明，在分析具有不同团簇大小的CRT时，尤其是当团簇数目较小时，Better-Inside方法在统计上比Satterthwaite或Kenward-Roger方法更有效。我们的结论是，当GLMM用于分析具有二元结果和较少异质团簇的CRT时，由于它的I型误差性质和相对较高的功率，应该推荐F检验的分母间自由度近似方法。
Small number of clusters and large variation of cluster sizes commonly exist in cluster-randomized trials (CRTs) and are often the critical factors affecting the validity and efficiency of statistical analyses. F tests are commonly used in the generalized linear mixed model (GLMM) to test intervention effects in CRTs. The most challenging issue for the approximate Wald F test is the estimation of the denominator degrees of freedom (DDF). Some DDF approximation methods have been proposed, but their small sample performances in analysing binary outcomes in CRTs with few heterogeneous clusters are not well studied. The small sample performances of five DDF approximations for the F test are compared and contrasted under CRT frameworks with simulations. Specifically, we illustrate how the intraclass correlation (ICC), sample size, and the variation of cluster sizes affect the type I error and statistical power when different DDF approximation methods in GLMM are used to test intervention effect in CRTs with binary outcomes. The results are also illustrated using a real CRT dataset. Our simulation results suggest that the Between-Within method maintains the nominal type I error rates even when the total number of clusters is as low as 10 and is robust to the variation of the cluster sizes. The Residual and Containment methods have inflated type I error rates when the cluster number is small (<30) and the inflation becomes more severe with increased variation in cluster sizes. In contrast, the Satterthwaite and Kenward-Roger methods can provide tests with very conservative type I error rates when the total cluster number is small (<30) and the conservativeness becomes more severe as variation in cluster sizes increases. Our simulations also suggest that the Between-Within method is statistically more powerful than the Satterthwaite or Kenward-Roger method in analysing CRTs with heterogeneous cluster sizes, especially when the cluster number is small. We conclude that the Between-Within denominator degrees of freedom approximation method for F tests should be recommended when the GLMM is used in analysing CRTs with binary outcomes and few heterogeneous clusters, due to its type I error properties and relatively higher power.
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作者：
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