Zhang Yi, Zheng Meng, Liang Xue-Zhi, Wang Qi, Wu Kun-Peng, Guo Ting-Ting, Chen Wen
Department of Medical Statistics, School of Public Health, Sun Yat-Sen University, Guangzhou, China.
Center for Migrant Health Policy, Sun Yat-Sen University, Guangzhou, China.
BMC Med Res Methodol. 2025 Apr 16;25(1):96. doi: 10.1186/s12874-025-02546-w.
The concurrent multiple-intervention stepped wedge design (M-SWD) is one of the most widely used variants of the SWD. We aimed to conduct power analysis for concurrent balanced (equal number of clusters in intervention groups) and imbalanced (unequal number of clusters in intervention groups) M-SWDs.
We conducted power analysis using a simulation-based approach with cross-sectional or closed-cohort designs and examined impact of design parameters (cluster size and number of clusters) and correlation parameters (total random effects variance (TRE), cluster autocorrelation coefficient (CAC), and individual autocorrelation coefficient (IAC)) on the powers of statistical tests for treatment effects.
With a fixed total sample size, increasing the number of clusters improves statistical power. When two treatment effects differ greatly, the concurrent imbalanced M-SWD saves sample size compared to the balanced design and powers could achieve the target value when the ratio of clusters approximates the inverse ratio of two effects. However, the allocation ratio should be no greater than 4:1. Additionally, statistical powers increased with decreasing TRE and increasing CAC and IAC. The impact of autocorrelation coefficients on powers is more pronounced when these parameters are large.
When two treatment effects differ greatly, the concurrent imbalanced M-SWD, with an allocation ratio no larger than 4:1, is a preferred design over the balanced one. For both concurrent balanced and imbalanced M-SWD, it is recommended to set large number of clusters with small cluster sizes and to carefully consider estimates of correlation parameters when designing the trial.
并行多干预阶梯楔形设计（M-SWD）是阶梯楔形设计中使用最广泛的变体之一。我们旨在对并行平衡（干预组中聚类数量相等）和不平衡（干预组中聚类数量不相等）的M-SWD进行效能分析。
我们采用基于模拟的方法，结合横断面或封闭队列设计进行效能分析，并研究设计参数（聚类大小和聚类数量）和相关参数（总随机效应方差（TRE）、聚类自相关系数（CAC）和个体自相关系数（IAC））对治疗效果统计检验效能的影响。
在总样本量固定的情况下，增加聚类数量可提高统计效能。当两种治疗效果差异很大时，与平衡设计相比，并行不平衡M-SWD可节省样本量，并且当聚类比例接近两种效果的反比时，效能可达到目标值。然而，分配比例应不大于4:1。此外，统计效能随TRE的降低以及CAC和IAC的增加而提高。当这些自相关系数较大时，它们对效能的影响更为显著。
当两种治疗效果差异很大时，分配比例不大于4:1的并行不平衡M-SWD是比平衡设计更优的选择。对于并行平衡和不平衡的M-SWD，建议设置大量小聚类，并在设计试验时仔细考虑相关参数的估计。
