Informative cluster size (ICS) and informative subgroup size (ISS) can distort marginal association estimates when the number of observed units, or their distribution across outcome-defined categories, is related to the outcomes under study. This issue is especially relevant for paired outcomes, where the observed association can depend on cluster size, paired-category composition, and the process by which units become available for analysis. We propose three weighted estimating approaches for marginal association between paired outcomes in clustered data. The weights are derived from within-cluster resampling arguments and extend inverse cluster-size and subgroup-size weighting to paired outcome categories. We also modify an existing ISS testing procedure by utilizing Stouffer's method to reduce computational burden. To evaluate the methods, we develop a simulator for clustered paired outcomes that separates unit-level association, latent cluster-level association, and outcome-dependent retention. Simulations show that pair-based weighting can reduce bias when association arises through unit-level dependence and subgroup composition is informative, but can attenuate association carried by latent cluster-level structure. Typical inverse-cluster weighting remains more stable when the association is primarily cluster-level. Application to NHANES oral-health data shows small positive periodontal and caries associations overall, with filled-surface outcomes showing stronger ISS evidence and greater sensitivity to pair-based weighting than decayed-surface outcomes. These results indicate that marginal association under ICS and ISS should be interpreted in relation to the source of association, observed-unit structure, and assumptions used to choose the weighting scheme.

翻译：信息性聚类大小（ICS）和信息性子组大小（ISS）可能在观测单元数量或其在不同结局类别中的分布与研究结局相关时扭曲边际关联估计。这一问题对配对结局尤为突出，因为观测到的关联可能依赖于聚类大小、配对类别构成以及单元被纳入分析的可用过程。我们提出三种加权估计方法，用于分析聚类数据中配对结局的边际关联。权重基于聚类内重抽样推导，并将逆聚类大小和子组大小加权扩展至配对结局类别。同时利用Stouffer方法改进现有ISS检验程序以降低计算负担。为评估方法性能，我们开发了聚类配对结局模拟器，可分离单元水平关联、潜在聚类水平关联与结局依赖性保留。模拟表明：当关联源于单元水平依赖性且子组构成具有信息性时，配对加权可减少偏倚，但可能削弱由潜在聚类水平结构承载的关联；当关联主要存在于聚类水平时，传统逆聚类加权仍保持更稳定。应用于NHANES口腔健康数据的分析显示，牙周病与龋齿之间总体呈现微弱正相关，与龋坏表面结局相比，充填表面结局显示出更强的ISS证据且对配对加权更敏感。结果表明，在存在ICS和ISS时，应结合关联来源、观测单元结构及选择加权方案的假设来解释边际关联。