Stepped wedge designs: insights from a design of experiments perspective
Stepped wedge designs: insights from a design of experiments perspective
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DOI：
10.1002/sim.7403
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发表时间：
2017-10-30
影响因子：
2
通讯作者：
Forbes, A. B.
中科院分区：
文献类型：
--
作者：
Matthews, J. N. S.;Forbes, A. B.
阶梯楔形设计（SWDs）最近受到了相当大的关注，因为它们可能是一种有用的方法来评估新的治疗方法，如卫生服务的实施。由于分配通常是按群组进行的，因此SWD通常被视为一种群组随机试验。然而，由于在研究过程中，一个群内的治疗会发生变化，因此也可以将其视为一种交叉设计。本文从交叉试验的角度探讨了SWD，并设计了更一般的实验。本文证明了线性混合效应模型中的处理效应估计量可以分解为两个估计量的加权平均值：（1）将SWD视为传统的行列设计;（2）所谓的垂直分析，即省略行效应的行列设计。这分别提供了横向和纵向比较的精确表示，迄今为止，文献中没有正式描述。这种分解显示了一种有时令人惊讶的分析方法，可以纠正时间和治疗效果之间的部分混淆。该方法还允许的混合效应模型中的相关参数的错误指定所造成的效率损失的量化。最佳的扩展的垂直分析，这些被证明是非常低效的集群内的依赖，可能会遇到在实践中的值。一些最近描述的扩展到经典的SWD纳入多种治疗方法也比较使用的实验设计框架。
Stepped wedge designs (SWDs) have received considerable attention recently, as they are potentially a useful way to assess new treatments in areas such as health services implementation. Because allocation is usually by cluster, SWDs are often viewed as a form of cluster-randomized trial. However, since the treatment within a cluster changes during the course of the study, they can also be viewed as a form of crossover design. This article explores SWDs from the perspective of crossover trials and designed experiments more generally. We show that the treatment effect estimator in a linear mixed effects model can be decomposed into a weighted mean of the estimators obtained from (1) regarding an SWD as a conventional row-column design and (2) a so-called vertical analysis, which is a row-column design with row effects omitted. This provides a precise representation of horizontal and vertical comparisons, respectively, which to date have appeared without formal description in the literature. This decomposition displays a sometimes surprising way the analysis corrects for the partial confounding between time and treatment effects. The approach also permits the quantification of the loss of efficiency caused by mis-specifying the correlation parameter in the mixed-effects model. Optimal extensions of the vertical analysis are obtained, and these are shown to be highly inefficient for values of the within-cluster dependence that are likely to be encountered in practice. Some recently described extensions to the classic SWD incorporating multiple treatments are also compared using the experimental design framework.