Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study
Minimum number of clusters and comparison of analysis methods for cross sectional stepped wedge cluster randomised trials with binary outcomes: A simulation study
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DOI：
10.1186/s13063-017-1862-2
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发表时间：
2017-03-09
期刊：
影响因子：
2.5
通讯作者：
McElduff, Patrick
中科院分区：
文献类型：
--
作者：
Barker, Daniel;D'Este, Catherine;McElduff, Patrick
背景：阶梯式楔形整群随机试验通常涉及数量相对较少的整群。用于分析这类试验数据的最常见框架是广义估计方程和广义线性混合模型。对这些方法进行大量研究的一个主题是，它们应用于对随机试验数据进行分类，特别是对干预效果做出合理推断所需的分类数量。然而，对于被许多研究人员声称比平行整群随机试验具有统计功效优势的阶梯楔形试验，所需的最小聚类数尚未被研究。方法：我们进行了一项模拟研究，其中我们考虑了文献中建议的最常用的方法来分析横断面阶梯楔形随机试验数据。我们比较了这些方法在阶梯楔形试验环境下的偏倚百分比、I类错误率和功效，以及两种结果，在这种情况下，可用的簇很少，当对时间趋势进行适当的调整时，这可能会混淆干预效果。结果：我们发现当可用的簇很少时，广义线性混合建模方法是最一致的。我们还发现，当只有3个类群时，没有一种常用的分析方法是无偏的，并且保持了5%的I型错误率。结论：在常用的分析方法中，我们建议将推广的线性混合模型应用于具有二元结果的小型阶梯试验。我们还建议在有三个步骤的阶梯式楔形设计中，每个步骤至少随机两个组，以确保干预效果估计器保持名义上5%的显著水平，并且也是合理的公正的。
Background: Stepped wedge cluster randomised trials frequently involve a relatively small number of clusters. The most common frameworks used to analyse data from these types of trials are generalised estimating equations and generalised linear mixed models. A topic of much research into these methods has been their application to cluster randomised trial data and, in particular, the number of clusters required to make reasonable inferences about the intervention effect. However, for stepped wedge trials, which have been claimed by many researchers to have a statistical power advantage over the parallel cluster randomised trial, the minimum number of clusters required has not been investigated.Methods: We conducted a simulation study where we considered the most commonly used methods suggested in the literature to analyse cross-sectional stepped wedge cluster randomised trial data. We compared the per cent bias, the type I error rate and power of these methods in a stepped wedge trial setting with a binary outcome, where there are few clusters available and when the appropriate adjustment for a time trend is made, which by design may be confounding the intervention effect.Results: We found that the generalised linear mixed modelling approach is the most consistent when few clusters are available. We also found that none of the common analysis methods for stepped wedge trials were both unbiased and maintained a 5% type I error rate when there were only three clusters.Conclusions: Of the commonly used analysis approaches, we recommend the generalised linear mixed model for small stepped wedge trials with binary outcomes. We also suggest that in a stepped wedge design with three steps, at least two clusters be randomised at each step, to ensure that the intervention effect estimator maintains the nominal 5% significance level and is also reasonably unbiased.