Cluster randomized trials are widely used when individual randomization is logistically infeasible or when correlations between observations cannot be ignored, especially in fields such as ophthalmology, infectious disease, vaccine research, and sociology. The desirability of outcome ranking (DOOR) framework evaluates patient-centric benefit-risk using an ordinal outcome and a Wilcoxon-Mann-Whitney statistic-based approach to compare outcome distributions between interventions. We propose a suite of new methods to extend DOOR to cluster trials based on properties of U-statistics and influence functions to estimate within-cluster and between-cluster treatment effects. These approaches can be applied in different scenarios, including mixtures of clusters with two treatment groups and clusters with only one group, and both small and large numbers of clusters. Simulations demonstrate that the proposed methods perform well under various scenarios regarding the number of clusters and cluster sizes. As an illustration, we apply the proposed methods to a cluster randomized crossover trial comparing delayed cord clamping and umbilical cord milking for newborns.

翻译：整群随机试验广泛应用于个体随机化不可行或观测间相关性不可忽略的场景，尤其在眼科学、传染病学、疫苗研究及社会学等领域。结局排名期望（DOOR）框架通过排序结局及基于Wilcoxon-Mann-Whitney统计量的方法评估以患者为中心的获益-风险比，以比较干预组间的结局分布。我们基于U统计量与影响函数的性质，提出一系列新方法将DOOR扩展至整群试验，用于估计群内与群间处理效应。这些方法可应用于不同场景，包括包含两个处理组的混合整群与仅含单一处理组的整群，以及小规模与大规模整群数。模拟结果表明，所提方法在不同整群数量与整群规模场景下均表现良好。作为案例，我们将所提方法应用于一项比较延迟脐带夹闭与脐带挤奶对新生儿影响的整群随机交叉试验。