Robust spatial models for clustered periodontal data

描述(由申请人提供)：牙周炎是一种慢性牙周炎性疾病，估计有50%的35岁以上的美国成年人受到影响。为了制定适当的治疗和治疗计划，牙科保健专业人员必须了解通过牙周进展影响牙齿脱落的生物、社会人口、行为和其他医学因素。然而，用来理解这些的统计方法伴随着几个有趣的挑战。这些数据是多变量的、非高斯的、非平稳的，并且有缺失的信息，这些信息可以反映该口腔区域的口腔健康状况。此外，可以推测牙周进展在空间上是可以参考的。目前的统计方法没有在统一的框架下处理所有这些问题。目标：使用贝叶斯范式，拟议的研究将开发结合所有上述挑战的稳健的统计方法，用于评估牙周病状况并确定相关的重要协变量。研究对象：统计方法将在313名齿状受试者的数据集上进行评估，这些受试者是作为SC Cobre口腔健康研究的一部分，参加Gullah非洲裔美国人(AA)糖尿病(GAAD)研究的。为便于推广，这些方法将根据作为国民健康状况分析系统(1999-2004年)的一部分收集的具有国家代表性的数据进行调查。可获得的数据和研究设计：作为Gaad研究的一部分，南卡罗来纳医科大学(MUSC)收集了牙周状况(由现场水平的口袋部门、临床附着水平和探诊时出血确定)、其他相关的生物和医疗状况，如吸烟习惯、刷牙和牙线清洁习惯、人口统计学(贫困状况)和其他参数。古拉-AA受试者代表了一个有趣的人群，他们的基因混合物很少，他们的牙齿健康状况仍然非常未知。NHANES的数据是公开的。意义：新的统计方法将为牙科研究人员提供更多关于在存在上述牙周数据典型特征的情况下，空间关联如何预测牙周进展的知识。这将使研究人员能够更有针对性地进行风险评估和预防战略，从而改善健康状况。

DESCRIPTION (provided by applicant): Periodontitis, a chronic inflammatory disease of the periodontium, affects an estimated 50% of US adults over the age of 35. To develop appropriate planning for treatment and cure, dental health care professionals must understand the biological, socio- demographic, behavioral and other medical factors that affect tooth loss through periodontal progression. However, the statistical methods employed to understand these come with several interesting challenges. The data are multivariate, non-Gaussian, non- stationary and have missing information which are informative of the oral health status of that oral region. Besides, one can conjecture that periodontal progression can be spatially referenced. Current statistical methods do not address all of these under a unified framework. Goals: Using a Bayesian paradigm, the proposed study will develop robust statistical methods combining all the above challenges, for assessing periodontal disease status and identifying important covariates that are associated. Subjects: The statistical methods will be evaluated on a dataset of 313 dentate subjects who were enrolled in the Gullah African-American (AA) Diabetics (GAAD) Study as part of the SC COBRE for Oral Health. For generalizability, the methods will be investigated on nationally-representative data collected as part of NHANES (1999-2004). Available data and study design: Periodontal status (determined by site-level pocket dept, clinical attachment level, and bleeding on probing), other relevant biological and medical status like smoking habits, brushing and flossing habits, demographics (poverty status) and other parameters have been collected at the Medical University of South Carolina (MUSC) as part of the GAAD study. The Gullah-AA subjects represent an interesting population with minimal genetic admixture whose dental health status remains vastly unknown. NHANES data are publicly available. Significance: The new statistical methods will provide dental researchers enhanced knowledge about how spatial associations might predict periodontal progression in presence of the aforementioned characteristics typical for periodontal data. This will enable researchers to better target risk assessment and prevention strategies, thereby improving health status.