Stepped-wedge cluster randomised trials (SW-CRTs) increasingly evaluate complex interventions, yet methodological guidance for analysing composite endpoints using generalized pairwise comparisons (GPC)remains limited. This work investigates the performance of several GPC-based estimators in the presence of clustering, temporal trends, and varying correlation structures typical of SW-CRTs. We conducted an extensive simulation study covering a range of intraclass correlations (ICC), cluster autocorrelation coefficients (CAC), time effects, and treatment effect sizes. Eight analytical approaches were compared, including unadjusted estimators, cluster-stratified win odds, mixed-effects models applied to cluster-period win odds, and probabilistic index models (PIMs). Type I error control was strongly compromised for methods ignoring time or clustering, whereas only two approaches consistently maintained nominal error rates: a hierarchical mixed-effects model with sequence and cluster-level random slopes (b4) and a cluster-restricted PIM (c2). These two methods were further evaluated in terms of statistical power, where c2 generally showed higher efficiency, particularly under strong clustering, low CAC, or the presence of temporal trends, while both converged to similar performance for large treatment effects. Overall, our findings identify b4 and c2 as the most reliable GPC-based strategies for SW-CRT analysis and provide practical guidance for their application, including for ongoing trials such as ETHER.

翻译：阶梯楔形集群随机试验（SW-CRTs）越来越多地用于评估复杂干预措施，然而使用广义配对比较（GPC）分析复合终点的方法学指导仍然有限。本研究探讨了在SW-CRTs典型的集群效应、时间趋势及不同相关性结构下，几种基于GPC的估计量的性能。我们开展了一项广泛的模拟研究，涵盖了一系列组内相关系数（ICC）、集群自相关系数（CAC）、时间效应和处理效应大小。共比较了八种分析方法，包括未调整的估计量、集群分层胜算比、应用于集群周期胜算比的混合效应模型以及概率指数模型（PIMs）。忽略时间或集群效应的方法其I类错误控制受到严重影响，而仅有两种方法能持续维持名义错误率：包含序列和集群水平随机斜率的层次混合效应模型（b4）以及集群限制性PIM（c2）。进一步评估了这两种方法的统计功效，其中c2通常表现出更高的效率，尤其是在强集群效应、低CAC或存在时间趋势的情况下，而当处理效应较大时，两者的性能趋于一致。总体而言，我们的研究结果确定b4和c2为SW-CRT分析中最可靠的基于GPC的策略，并为其应用提供了实用指导，包括针对诸如ETHER等正在进行中的试验。