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值间相关性不可忽略的场景，尤其在眼科、传染病学、疫苗研究及社会学等领域。结果排序可取性（DOOR）框架通过有序结局及基于Wilcoxon-Mann-Whitney统计量的方法评估以患者为中心的获益-风险比，从而比较干预组间的结局分布。基于U统计量与影响函数的性质，我们提出一套新方法将DOOR扩展至整群试验，用于估计群内与群间处理效应。这些方法可适用于不同场景，包括包含两个治疗组的混合整群、仅含单一治疗组的整群，以及整群数量较少或较多的情况。模拟结果表明，在多种整群数量与群体规模设定下，所提方法均表现良好。为作示例，我们将所提方法应用于一项比较延迟脐带夹闭与脐带挤压对新生儿影响的整群随机交叉试验。