Hub for Trials Methodology Research, MRC Biostatistics Unit, University of Cambridge, Cambridge, UK.
Institute of Health and Society, Newcastle University, Newcastle, UK.
Stat Med. 2019 Mar 30;38(7):1103-1119. doi: 10.1002/sim.8022. Epub 2018 Nov 6.
Numerous publications have now addressed the principles of designing, analyzing, and reporting the results of stepped-wedge cluster randomized trials. In contrast, there is little research available pertaining to the design and analysis of multiarm stepped-wedge cluster randomized trials, utilized to evaluate the effectiveness of multiple experimental interventions. In this paper, we address this by explaining how the required sample size in these multiarm trials can be ascertained when data are to be analyzed using a linear mixed model. We then go on to describe how the design of such trials can be optimized to balance between minimizing the cost of the trial and minimizing some function of the covariance matrix of the treatment effect estimates. Using a recently commenced trial that will evaluate the effectiveness of sensor monitoring in an occupational therapy rehabilitation program for older persons after hip fracture as an example, we demonstrate that our designs could reduce the number of observations required for a fixed power level by up to 58%. Consequently, when logistical constraints permit the utilization of any one of a range of possible multiarm stepped-wedge cluster randomized trial designs, researchers should consider employing our approach to optimize their trials efficiency.
现在已经有许多出版物讨论了设计、分析和报告阶梯式群组随机试验结果的原则。相比之下，关于多臂阶梯式群组随机试验的设计和分析的研究很少，这些试验用于评估多种实验干预措施的效果。在本文中，我们将通过解释当使用线性混合模型分析数据时，如何确定这些多臂试验所需的样本量来解决这个问题。然后，我们继续描述如何优化这些试验的设计，以在最小化试验成本和最小化处理效果估计协方差矩阵的某个函数之间取得平衡。我们使用最近开始的一项试验来说明，该试验将评估传感器监测在老年人髋部骨折后职业治疗康复计划中的有效性，我们证明我们的设计可以将固定功效水平所需的观测次数减少多达 58%。因此，当逻辑约束允许使用一系列可能的多臂阶梯式群组随机试验设计中的任何一种时，研究人员应该考虑采用我们的方法来优化试验效率。
