Robust spatial models for clustered periodontal data

描述（由申请人提供）：牙周炎是牙周组织的一种慢性炎症性疾病，影响了大约50%的35岁以上的美国成年人。为了制定适当的治疗和治愈计划，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学、社会人口统计学、行为和其他医学因素。然而，用于理解这些问题的统计方法面临着一些有趣的挑战。这些数据是多元的、非高斯的、非平稳的，并且缺少有关该口腔区域的口腔健康状况的信息。此外，人们可以推测牙周的进展可以在空间上参考。目前的统计方法没有在一个统一的框架下处理所有这些问题。目标：使用贝叶斯范式，提出的研究将开发强大的统计方法，结合上述所有挑战，评估牙周病状态并识别相关的重要协变量。受试者：统计方法将在313名有牙受试者的数据集上进行评估，这些受试者是作为口腔健康SC COBRE的一部分，参加了Gullah非裔美国人（AA）糖尿病（GAAD）研究。为了推广，将对作为NHANES（1999-2004）的一部分收集的具有全国代表性的数据进行调查。可用数据和研究设计：作为GAAD研究的一部分，在南卡罗来纳医科大学（MUSC）收集了牙周状况（由牙袋深度、临床附着程度和探查时出血决定）、其他相关的生物学和医学状况，如吸烟习惯、刷牙和使用牙线的习惯、人口统计（贫困状况）和其他参数。gulla - aa受试者代表了一个有趣的人群，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关于空间关联如何在上述牙周数据典型特征的存在下预测牙周进展的知识。这将使研究人员能够更好地针对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.