Treatment effect estimands based on win statistics, including the win ratio, win odds, and win difference are increasingly popular targets for summarizing endpoints in clinical trials. Such win estimands offer an intuitive approach for prioritizing outcomes by clinical importance. The implementation and interpretation of win estimands is complicated in cluster randomized trials (CRTs), where researchers can target fundamentally different estimands on the individual-level or cluster-level. We numerically demonstrate that individual-pair and cluster-pair win estimands can substantially differ when cluster size is informative: where outcomes and/or treatment effects depend on cluster size. With such informative cluster sizes, individual-pair and cluster-pair win estimands can even yield opposite conclusions regarding treatment benefit. We describe consistent estimators for individual-pair and cluster-pair win estimands and propose a leave-one-cluster-out jackknife variance estimator for inference. Despite being consistent, our simulations highlight that some caution is needed when implementing individual-pair win estimators due to finite-sample bias. In contrast, cluster-pair win estimators are unbiased for their respective targets. Altogether, careful specification of the target estimand is essential when applying win estimators in CRTs. Failure to clearly define whether individual-pair or cluster-pair win estimands are of primary interest may result in answering a dramatically different question than intended.

翻译：基于胜出统计量（包括胜出比、胜出优势比和胜出差）的治疗效应估计目标，正日益成为临床试验中总结终点的常用指标。此类胜出估计量提供了一种按临床重要性对结局进行优先排序的直观方法。在整群随机试验中，胜出估计量的实施与解释变得复杂，因为研究者可能针对个体层面或整群层面截然不同的估计目标。我们通过数值模拟证明，当群组规模具有信息性（即结局和/或治疗效果依赖于群组规模）时，个体配对与整群配对的胜出估计目标可能存在显著差异。在此类信息性群组规模条件下，个体配对与整群配对的胜出估计目标甚至可能得出关于治疗获益的相反结论。我们描述了针对个体配对与整群配对胜出估计目标的一致性估计量，并提出了一种用于统计推断的留一聚类刀切法方差估计量。尽管具有一致性，我们的模拟结果表明，在实施个体配对胜出估计量时，由于有限样本偏倚的存在仍需保持谨慎。相比之下，整群配对胜出估计量对其相应目标具有无偏性。总之，在整群随机试验中应用胜出估计量时，必须谨慎明确目标估计量。若未能清晰界定主要关注的是个体配对还是整群配对胜出估计目标，可能导致回答的问题与预期目标产生根本性偏差。