Department of Biostatistics, Bioinformatics and Epidemiology, Medical University of South Carolina, Charleston, SC 29425, USA.
Stat Med. 2009 Dec 10;28(28):3492-508. doi: 10.1002/sim.3647.
Dental research gives rise to data with potentially complex correlation structure. Assessments of dental caries yield a binary outcome indicating the presence or absence of caries experience for each surface of each tooth in a subject's mouth. In addition to this nesting, caries outcome exhibit spatial structure among neighboring teeth. We develop a Bayesian multivariate model for spatial binary data using random effects autologistic regression that controls for the correlation within tooth surfaces and spatial correlation among neighboring teeth. Using a sample from a clinical study conducted at the Medical University of South Carolina, we compare this autologistic model with covariates to alternative models to demonstrate the improvement in predictions and also to assess the effects of covariates on caries experience.
口腔研究产生的数据具有潜在的复杂相关结构。龋齿评估的结果是一个二元结果，表明受检者口腔中每个牙齿的每个表面是否有龋齿。除了这种嵌套关系，龋齿的结果在相邻牙齿之间存在空间结构。我们使用随机效应自回归回归方法为空间二元数据开发了一个贝叶斯多变量模型，该方法控制了牙齿表面内的相关性以及相邻牙齿之间的空间相关性。我们使用南卡罗来纳医科大学进行的一项临床研究的样本，将这种自回归模型与协变量进行比较，以展示对预测的改进，并评估协变量对龋齿的影响。
