Stepped wedge cluster trials are an increasingly popular alternative to traditional parallel cluster randomized trials. Such trials often utilize a small number of clusters and numerous time intervals, and these components must be considered when choosing an analysis method. A generalized linear mixed model containing a random intercept and fixed time and intervention covariates is the most common analysis approach. However, the sole use of a random intercept applies a constant intraclass correlation coefficient structure, which is an assumption that is likely to be violated given stepped wedge trials (SWTs) have multiple time intervals. Alternatively, generalized estimating equations (GEE) are robust to the misspecification of the working correlation structure, although it has been shown that small‐sample adjustments to standard error estimates and the use of appropriate degrees of freedom are required to maintain the validity of inference when the number of clusters is small. In this article, we show, using an extensive simulation study based on a motivating example and a more general design, the use of GEE can maintain the validity of inference in small‐sample SWTs with binary outcomes. Furthermore, we show which combinations of bias corrections to standard error estimates and degrees of freedom work best in terms of attaining nominal type I error rates.

中文翻译：

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使用广义估计方程时，在具有二元结果的小样本阶梯楔形聚类随机试验中，保持推论的有效性。

逐步楔形聚类试验是替代传统平行聚类随机试验的一种日益流行的替代方法。这样的试验通常利用少量的簇和大量的时间间隔，并且在选择分析方法时必须考虑这些组成部分。包含随机截距，固定时间和干预协变量的广义线性混合模型是最常见的分析方法。但是，仅使用随机截距可应用恒定的类内相关系数结构，这是一个假设，因为阶梯式楔形试验（SWT）具有多个时间间隔，很可能会违反该假设。另外，广义估计方程（GEE）对于工作相关结构的错误指定具有鲁棒性，尽管已经表明，当簇数较少时，需要对标准误差估计值进行小样本调整并使用适当的自由度，以保持推理的有效性。在本文中，我们通过基于激励示例和更一般设计的广泛模拟研究，表明GEE的使用可以在具有二进制结果的小样本SWT中保持推理的有效性。此外，我们显示了对标准误差估计值和自由度进行偏差校正的哪种组合在达到标称I类错误率方面效果最好。GEE的使用可以在具有二进制结果的小样本SWT中保持推理的有效性。此外，我们显示了对标准误差估计值和自由度进行偏差校正的哪种组合在达到标称I类错误率方面效果最好。GEE的使用可以在具有二进制结果的小样本SWT中保持推理的有效性。此外，我们显示了对标准误差估计值和自由度进行偏差校正的哪种组合在达到标称I类错误率方面效果最好。