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首页> 外文期刊>BMC Medical Research Methodology >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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Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials

机译：在分析小样本集群随机试验中的二元结果时，比较广义线性混合模型的分母自由度近似值

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Background 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. Methods 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. Results 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. Conclusion 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.
机译：背景技术群集随机试验（CRT）中通常存在群集数量少和群集大小变化大的情况，并且通常是影响统计分析有效性和效率的关键因素。广义线性混合模型（GLMM）中通常使用F检验来测试CRT中的干预效果。近似Wald F检验最具挑战性的问题是分母自由度（DDF）的估计。有人提出了一些DDF近似方法，但对于异构簇很少的CRT中二元结果的分析，其小样本性能并未得到很好的研究。方法在CRT框架下，将5种DDF近似值用于F检验的小样本性能进行比较和对比。具体来说，当使用GLMM中不同的DDF近似方法测试具有二元结果的CRT的干预效果时，我们说明了类内相关性（ICC），样本量和簇大小的变化如何影响I型误差和统计功效。还使用真实的CRT数据集说明了结果。结果我们的模拟结果表明，即使簇的总数低至10，“内部之间”方法也可以保持名义I型错误率，并且对于簇大小的变化具有鲁棒性。当簇数较小（<30）时，“残差”和“容纳”方法会增加I型错误率，并且随着簇大小变化的增加，充气会变得更加严重。相比之下，当群集总数较小（<30）并且随着群集大小变化的增加而变得更加严重时，Satterthwaite和Kenward-Roger方法可以为I型错误率提供非常保守的测试。我们的模拟还表明，在分析具有不同簇大小的CRT时，尤其是当簇数较小时，“内部之间”方法在统计上比Satterthwaite或Kenward-Roger方法更强大。结论我们得出的结论是，由于GLMM具有I型错误特性和相对较高的功效，因此当将GLMM用于分析具有二元结果且异质簇很少的CRT时，应建议使用F分母内部近似自由度方法。

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