PDF(Pubmed)
Abstract：
BACKGROUND: The analysis of dental caries has been a major focus of recent work on modeling dental defect data. While a dental caries focus is of major importance in dental research, the examination of developmental defects which could also contribute at an early stage of dental caries formation, is also of potential interest. This paper proposes a set of methods which address the appearance of different combinations of defects across different tooth regions. In our modeling we assess the linkages between tooth region development and both the type of defect and associations with etiological predictors of the defects which could be influential at different times during the tooth crown development.
METHODS: We develop different hierarchical model formulations under the Bayesian paradigm to assess exposures during primary central incisor (PMCI) tooth development and PMCI defects. We evaluate the Bayesian hierarchical models under various simulation scenarios to compare their performance with both simulated dental defect data and real data from a motivating application.
RESULTS: The proposed model provides inference on identifying a subset of etiological predictors of an individual defect accounting for the correlation between tooth regions and on identifying a subset of etiological predictors for the joint effect of defects. Furthermore, the model provides inference on the correlation between the regions of the teeth as well as between the joint effect of the developmental enamel defects and dental caries. Simulation results show that the proposed model consistently yields steady inferences in identifying etiological biomarkers associated with the outcome of localized developmental enamel defects and dental caries under varying simulation scenarios as deemed by small mean square error (MSE) when comparing the simulation results to real application results.
CONCLUSIONS: We evaluate the proposed model under varying simulation scenarios to develop a model for multivariate dental defects and dental caries assuming a flexible covariance structure that can handle regional and joint effects. The proposed model shed new light on methods for capturing inclusive predictors in different multivariate joint models under the same covariance structure and provides a natural extension to a nested hierarchical model.
METHODS: We develop different hierarchical model formulations under the Bayesian paradigm to assess exposures during primary central incisor (PMCI) tooth development and PMCI defects. We evaluate the Bayesian hierarchical models under various simulation scenarios to compare their performance with both simulated dental defect data and real data from a motivating application.
RESULTS: The proposed model provides inference on identifying a subset of etiological predictors of an individual defect accounting for the correlation between tooth regions and on identifying a subset of etiological predictors for the joint effect of defects. Furthermore, the model provides inference on the correlation between the regions of the teeth as well as between the joint effect of the developmental enamel defects and dental caries. Simulation results show that the proposed model consistently yields steady inferences in identifying etiological biomarkers associated with the outcome of localized developmental enamel defects and dental caries under varying simulation scenarios as deemed by small mean square error (MSE) when comparing the simulation results to real application results.
CONCLUSIONS: We evaluate the proposed model under varying simulation scenarios to develop a model for multivariate dental defects and dental caries assuming a flexible covariance structure that can handle regional and joint effects. The proposed model shed new light on methods for capturing inclusive predictors in different multivariate joint models under the same covariance structure and provides a natural extension to a nested hierarchical model.
摘要：
背景：龋齿分析一直是最近对牙齿缺陷数据建模工作的主要重点。虽然龋齿的焦点在牙科研究中非常重要，发育缺陷的检查也可能在龋齿形成的早期阶段作出贡献，也有潜在的兴趣。本文提出了一套方法，解决了不同牙齿区域缺陷的不同组合的出现。在我们的建模中，我们评估了牙齿区域发育与缺陷类型之间的联系以及与缺陷病因预测因子之间的联系，这些因素可能在牙冠发育的不同时间产生影响。
方法：我们在贝叶斯范式下开发了不同的分层模型公式，以评估主要中切牙（PMCI）牙齿发育和PMCI缺陷期间的暴露。我们在各种模拟场景下评估贝叶斯分层模型，以将其性能与模拟牙齿缺陷数据和来自激励应用程序的真实数据进行比较。
结果：所提出的模型提供了关于识别个体缺损的病因学预测因子子集的推断，这些预测因子解释了牙齿区域之间的相关性，以及识别了缺陷联合效应的病因学预测因子子集的推断。此外，该模型提供了有关牙齿区域之间以及发育牙釉质缺陷与龋齿的联合作用之间的相关性的推断。模拟结果表明，在将模拟结果与实际应用结果进行比较时，所提出的模型在不同的模拟场景下识别与局部发育牙釉质缺陷和龋齿的结果相关的病因生物标志物时始终产生稳定的推断，这被认为是小的均方误差（MSE）。
结论：我们在不同的模拟场景下评估了所提出的模型，以开发一种假设可处理区域和关节效应的灵活协方差结构的多变量牙齿缺陷和龋齿模型。所提出的模型为在相同协方差结构下在不同多变量联合模型中捕获包容性预测因子的方法提供了新的思路，并为嵌套分层模型提供了自然扩展。
方法：我们在贝叶斯范式下开发了不同的分层模型公式，以评估主要中切牙（PMCI）牙齿发育和PMCI缺陷期间的暴露。我们在各种模拟场景下评估贝叶斯分层模型，以将其性能与模拟牙齿缺陷数据和来自激励应用程序的真实数据进行比较。
结果：所提出的模型提供了关于识别个体缺损的病因学预测因子子集的推断，这些预测因子解释了牙齿区域之间的相关性，以及识别了缺陷联合效应的病因学预测因子子集的推断。此外，该模型提供了有关牙齿区域之间以及发育牙釉质缺陷与龋齿的联合作用之间的相关性的推断。模拟结果表明，在将模拟结果与实际应用结果进行比较时，所提出的模型在不同的模拟场景下识别与局部发育牙釉质缺陷和龋齿的结果相关的病因生物标志物时始终产生稳定的推断，这被认为是小的均方误差（MSE）。
结论：我们在不同的模拟场景下评估了所提出的模型，以开发一种假设可处理区域和关节效应的灵活协方差结构的多变量牙齿缺陷和龋齿模型。所提出的模型为在相同协方差结构下在不同多变量联合模型中捕获包容性预测因子的方法提供了新的思路，并为嵌套分层模型提供了自然扩展。
