A tutorial on sample size calculation for multiple-period cluster randomized parallel, cross-over and stepped-wedge trials using the Shiny CRT Calculator
A tutorial on sample size calculation for multiple-period cluster randomized parallel, cross-over and stepped-wedge trials using the Shiny CRT Calculator
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DOI：
10.1093/ije/dyz237
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发表时间：
2020-06-01
影响因子：
7.7
通讯作者：
Taljaard, Monica
中科院分区：
文献类型：
--
作者：
Hemming, Karla;Kasza, Jessica;Taljaard, Monica
长期以来，人们一直认为，集群随机试验的样本量计算需要考虑同一集群内的多个观察之间的相关性。当测量在任何时间点而不是在单个时间点进行时，这些相关性不仅取决于集群，而且还取决于测量之间的时间间隔，此外，取决于是否重复测量不同的参与者（横断面设计）或相同的参与者（队列设计）。这在具有多个测量期的试验中特别相关，例如群集交叉和阶梯楔形设计，但在平行设计中也有一定程度的相关性。已经发表了几篇描述这些设计的样本量方法的论文，但这种方法可能并不适合所有研究人员。在本文中，我们提供了有关集群随机设计的样本量计算的教程，特别强调具有多个测量周期的设计，并提供了基于网络的工具（Shiny CRT计算器），以使研究人员能够轻松进行这些样本量计算。我们考虑了横断面和队列设计，并允许各种假设的群内相关结构。我们考虑了治疗效果的聚类异质性（对于治疗与聚类交叉的设计），以及组间具有差异聚类的个体随机分组治疗试验，例如，聚类来自于分组提供的干预措施的设计。计算器将计算功率或精度，作为集群大小或集群数量的函数，用于各种各样的设计和相关结构。我们说明的方法和灵活性的闪亮CRT计算器使用一系列的例子。
It has long been recognized that sample size calculations for cluster randomized trials require consideration of the correlation between multiple observations within the same cluster. When measurements are taken at anything other than a single point in time, these correlations depend not only on the cluster but also on the time separation between measurements and additionally, on whether different participants (cross-sectional designs) or the same participants (cohort designs) are repeatedly measured. This is particularly relevant in trials with multiple periods of measurement, such as the cluster cross-over and stepped-wedge designs, but also to some degree in parallel designs. Several papers describing sample size methodology for these designs have been published, but this methodology might not be accessible to all researchers. In this article we provide a tutorial on sample size calculation for cluster randomized designs with particular emphasis on designs with multiple periods of measurement and provide a web-based tool, the Shiny CRT Calculator, to allow researchers to easily conduct these sample size calculations. We consider both cross-sectional and cohort designs and allow for a variety of assumed within-cluster correlation structures. We consider cluster heterogeneity in treatment effects (for designs where treatment is crossed with cluster), as well as individually randomized group-treatment trials with differential clustering between arms, for example designs where clustering arises from interventions being delivered in groups. The calculator will compute power or precision, as a function of cluster size or number of clusters, for a wide variety of designs and correlation structures. We illustrate the methodology and the flexibility of the Shiny CRT Calculator using a range of examples.