Marginal analysis of ordinal clustered longitudinal data with informative cluster size
Marginal analysis of ordinal clustered longitudinal data with informative cluster size
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DOI：
10.1111/biom.13050
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发表时间：
2019-09-01
期刊：
影响因子：
1.9
通讯作者：
Nelson, Kerrie P.
中科院分区：
文献类型：
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作者：
Mitani, Aya A.;Kaye, Elizabeth K.;Nelson, Kerrie P.
在牙科数据的分析中，经常会出现信息簇大小（ICS）的问题。ICS描述了感兴趣的结果与集群大小相关的情况。在具有潜在ICS的纵向研究中，大部分关于边际推理建模的工作都集中在连续结果上。然而，牙周病的结果，包括临床附着丧失，通常使用顺序评分系统进行评估。此外，参与者可能会在研究过程中由于疾病进展而失去牙齿。在这里，我们开发了纵向聚类加权广义估计方程（CWGEE）来模拟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）之间的关联，并通过拟合比例优势logistic回归模型来潜在地降低由于牙齿脱落导致的聚类大小。使用两阶段准最小二乘法估计随时间推移的齿内相关系数。我们工作的动机源于退伍军人事务部牙科纵向研究，参与者定期接受一般和口腔健康检查。在一个广泛的模拟研究中，我们比较了从CWGEE与各种工作相关结构，从传统的GEE不占ICS获得的结果。我们提出的方法产生的结果与传统的广义估计方程的方法相比，具有非常低的偏差和良好的覆盖概率。
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 fromCWGEE 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.