Maintaining the validity of inference in small-sample stepped wedge cluster randomized trials with binary outcomes when using generalized estimating equations
Maintaining the validity of inference in small-sample stepped wedge cluster randomized trials with binary outcomes when using generalized estimating equations
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DOI：
10.1002/sim.8575
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发表时间：
2020-06-23
影响因子：
2
通讯作者：
Westgate, Philip M.
中科院分区：
文献类型：
--
作者：
Ford, Whitney P.;Westgate, Philip M.
阶梯式楔形群试验是传统平行群随机试验的一种日益流行的替代方案。这类试验通常使用少量的集群和大量的时间间隔，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用的分析方法。然而，单独使用随机截取适用于恒定的组内相关系数结构，这一假设很可能在阶梯楔形试验(SWTs)具有多个时间间隔的情况下被违反。或者，广义估计方程(GEE)对工作相关结构的错误指定是稳健的，尽管已经表明，当簇数较小时，需要对标准误差估计进行小样本调整并使用适当的自由度来保持推断的有效性。在这篇文章中，我们使用基于激励实例和更一般的设计的广泛的模拟研究，在具有两种结果的小样本中，使用GEE可以保持推理的有效性。此外，我们还展示了在获得标称I类错误率方面，哪种偏差校正对标准误差估计和自由度的组合效果最好。
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.