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Bayesian latent variable models for spatially correlated tooth-level binary data in caries research
Statistical Modelling ( IF 1 ) Pub Date : 2011-01-10 , DOI: 10.1177/1471082x1001100103
Y Zhang 1 , D Todem , K Kim , E Lesaffre
Affiliation  

Analysis of dental caries is traditionally based on aggregated scores, which are summaries of caries experience for each individual. A well-known example of such scores is the decayed, missing and filled teeth or tooth surfaces index introduced in the 1930s. Although these scores have improved our understanding of the pattern of dental caries, there are still some fundamental questions that remain unanswered. As an example, it is well believed among dentists that there are spatial symmetries in the mouth with respect to caries, but this has never been evaluated in a statistical sense. An answer to this question requires the analysis to be performed at subunits within the mouth, which necessitates the use of methods for correlated data. We propose a Bayesian generalized latent variable model coupled with an undirected graphical model to investigate the unique spatial distribution of tooth-level caries outcomes in the mouth. Data from the Signal Tandmobiel® study in Flanders, a dental longitudinal survey, are used to illustrate the methodology.

中文翻译:

龋病研究中空间相关牙齿级二进制数据的贝叶斯潜变量模型

龋齿的分析传统上是基于汇总分数,这是每个人的龋齿经历的总结。此类评分的一个众所周知的例子是 1930 年代引入的腐烂、缺失和填充的牙齿或牙齿表面指数。尽管这些评分提高了我们对龋齿模式的理解,但仍有一些基本问题没有得到解答。例如,牙医普遍认为口腔中存在与龋齿相关的空间对称性,但这从未在统计意义上进行过评估。这个问题的答案需要在口腔内的亚基上进行分析,这需要使用相关数据的方法。我们提出了一个贝叶斯广义潜变量模型和一个无向图模型,以研究口腔中牙齿水平龋齿结果的独特空间分布。法兰德斯的 Signal Tandmobiel® 研究数据(一项牙科纵向调查)用于说明该方法。
更新日期:2011-01-10
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