Clustered dental data commonly arise when multiple teeth or tooth sites are observed within the same individual. In such settings, the number of observed units within a cluster may be informative, since tooth loss and missing measurements often reflect underlying oral health status. Standard marginal association measures may therefore be biased when larger or smaller clusters contribute disproportionate information. This paper develops weighted estimators for marginal association between paired tooth-level outcomes in the presence of informative cluster size and informative within-cluster subgroup structure. The proposed approach extends the logic of within-cluster resampling and cluster-weighted estimating equations to paired bivariate outcomes by constructing weights that balance contributions across clusters, observed marginal categories, and observed paired categories. Weighted estimating equations are used to estimate moment, rank, and cell-probability functionals, yielding clustered-data analogues of Pearson, Spearman, and phi association measures. Sandwich variance estimators and delta-method standard errors are derived for inference. Simulation studies assess finite-sample bias, standard error estimation, and coverage under varying sources of cluster-level and unit-level dependence, as well as outcome-dependent observation mechanisms. The methods are illustrated using tooth-level periodontal and caries outcomes from NHANES, where informative subgroup-size diagnostics indicate that the observed distribution of disease severity is not independent of within-mouth structure. The proposed estimators provide a principled basis for estimating marginal oral-health associations for a typical tooth from a typical individual, while reducing bias induced by informative tooth retention and subgroup composition.

翻译：在牙科研究中，当同一受试者体内观测到多颗牙齿或牙位时，常产生聚类数据。在此类场景中，聚类内观测单元的数量可能具有信息性，因为牙齿缺失和测量遗漏往往反映了潜在的口腔健康状况。因此，当较大或较小的聚类贡献不成比例的信息时，标准边际关联测量可能产生偏倚。本文发展了一种加权估计方法，用于在存在信息性聚类大小和信息性子组结构的配对牙齿水平结局之间估计边际关联。所提方法通过构建跨聚类、观测边际类别及观测配对类别间平衡贡献的权重，将聚类内重抽样和聚类加权估计方程的逻辑扩展至配对双变量结局。采用加权估计方程估计矩、秩和单元概率泛函，从而得到聚类数据对应的皮尔逊、斯皮尔曼和φ关联测量。推导了推理所用的三明治方差估计量和德尔塔法标准误。模拟研究评估了不同聚类水平和单元水平依赖性来源以及结局依赖性观测机制下的有限样本偏倚、标准误估计和覆盖概率。利用NHANES的牙齿水平牙周病和龋齿结局数据展示该方法，其中信息性子组大小诊断显示疾病严重程度的观测分布与口腔内结构并非独立。所提估计量为典型个体中典型牙齿的边际口腔健康关联估计提供了原理性基础，同时降低了由信息性牙齿留存和子组组成引起的偏倚。