Cluster randomized trials with a small number of clusters: which analyses should be used?
Cluster randomized trials with a small number of clusters: which analyses should be used?
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DOI：
10.1093/ije/dyx169
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发表时间：
2018-02-01
影响因子：
7.7
通讯作者：
Kahan, Brennan C.
中科院分区：
文献类型：
--
作者：
Leyrat, Clemence;Morgan, Katy E.;Kahan, Brennan C.
背景：整群随机试验（CRT）越来越多地用于评估健康干预措施的有效性。三种主要的分析方法是：簇级分析、混合模型和广义估计方程（GEE）。混合模型和 GEE 可能会导致少量集群导致 I 类错误率过高，并且已经提出了许多小样本校正来规避此问题。然而，这些方法对功耗的影响仍不清楚。 方法：我们进行了一项模拟研究，以评估具有连续结果和 40 个或更少簇的 CRT 的 12 种分析方法的性能。其中包括加权和未加权的簇级分析、具有不同自由度校正的混合效应模型，以及具有和不具有小样本校正的 GEE。我们通过不同的类内相关系数 (ICC) 值、聚类数量和聚类大小的变异性来评估这些方法。结果：未加权和方差加权聚类级别分析、具有自由度校正的混合模型以及具有小样本校正的 GEE 在大多数情况下都将 I 类错误率维持在 5% 或以下，而未校正的方法会导致 I 类错误率夸大。然而，当随机化的聚类少于 20 个时，这些分析的功效较低（在某些情况下低于 50%），没有一个达到预期的 80% 功效。结论：建议使用小样本校正或方差加权聚类水平分析来分析具有少量聚类的 CRT 中的连续结果。这些修正的使用应纳入样本量计算中，以防止研究动力不足。
Background: Cluster randomized trials (CRTs) are increasingly used to assess the effectiveness of health interventions. Three main analysis approaches are: cluster-level analyses, mixed-models and generalized estimating equations (GEEs). Mixed models and GEEs can lead to inflated type I error rates with a small number of clusters, and numerous small-sample corrections have been proposed to circumvent this problem. However, the impact of these methods on power is still unclear.Methods: We performed a simulation study to assess the performance of 12 analysis approaches for CRTs with a continuous outcome and 40 or fewer clusters. These included weighted and unweighted cluster-level analyses, mixed-effects models with different degree-of-freedom corrections, and GEEs with and without a small-sample correction. We assessed these approaches across different values of the intraclass correlation coefficient (ICC), numbers of clusters and variability in cluster sizes.Results: Unweighted and variance-weighted cluster-level analysis, mixed models with degree-of-freedom corrections, and GEE with a small-sample correction all maintained the type I error rate at or below 5% across most scenarios, whereas uncorrected approaches lead to inflated type I error rates. However, these analyses had low power (below 50% in some scenarios) when fewer than 20 clusters were randomized, with none reaching the expected 80% power.Conclusions: Small-sample corrections or variance-weighted cluster-level analyses are recommended for the analysis of continuous outcomes in CRTs with a small number of clusters. The use of these corrections should be incorporated into the sample size calculation to prevent studies from being underpowered.