Mitani Aya A, Kaye Elizabeth K, Nelson Kerrie P
Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, 02118.
Department of Health Policy and Health Services Research, Boston University Henry M. Goldman School of Dental Medicine, Boston, Massachusetts, 02118.
Biometrics. 2019 Sep;75(3):938-949. doi: 10.1111/biom.13050. Epub 2019 Apr 4.
The issue of informative cluster size (ICS) often arises in the analysis of dental data. ICS describes a situation where the outcome of interest is related to cluster size. Much of the work on modeling marginal inference in longitudinal studies with potential ICS has focused on continuous outcomes. However, periodontal disease outcomes, including clinical attachment loss, are often assessed using ordinal scoring systems. In addition, participants may lose teeth over the course of the study due to advancing disease status. Here we develop longitudinal cluster-weighted generalized estimating equations (CWGEE) to model the association of ordinal clustered longitudinal outcomes with participant-level health-related covariates, including metabolic syndrome and smoking status, and potentially decreasing cluster size due to tooth-loss, by fitting a proportional odds logistic regression model. The within-teeth correlation coefficient over time is estimated using the two-stage quasi-least squares method. The motivation for our work stems from the Department of Veterans Affairs Dental Longitudinal Study in which participants regularly received general and oral health examinations. In an extensive simulation study, we compare results obtained from CWGEE with various working correlation structures to those obtained from conventional GEE which does not account for ICS. Our proposed method yields results with very low bias and excellent coverage probability in contrast to a conventional generalized estimating equations approach.
在牙科数据分析中，信息性聚类大小（ICS）的问题经常出现。ICS描述了一种感兴趣的结果与聚类大小相关的情况。在具有潜在ICS的纵向研究中，许多关于建模边际推断的工作都集中在连续结果上。然而，包括临床附着丧失在内的牙周疾病结果通常使用有序评分系统进行评估。此外，在研究过程中，参与者可能会由于疾病进展而失去牙齿。在此，我们开发了纵向聚类加权广义估计方程（CWGEE），通过拟合比例优势逻辑回归模型，对有序聚类纵向结果与参与者水平的健康相关协变量（包括代谢综合征和吸烟状况）以及由于牙齿脱落可能导致的聚类大小减小之间的关联进行建模。使用两阶段准最小二乘法估计牙齿内部随时间的相关系数。我们这项工作源自美国退伍军人事务部牙科纵向研究，在该研究中参与者定期接受一般和口腔健康检查。在一项广泛的模拟研究中，我们将从具有各种工作相关结构的CWGEE获得的结果与从不考虑ICS的传统广义估计方程（GEE）获得的结果进行比较。与传统的广义估计方程方法相比，我们提出的方法产生的结果偏差非常低且覆盖概率极佳。
