Robust spatial models for clustered periodontal data

牙周聚类数据的稳健空间模型

• 批准号: 8319854
• 负责人: Dipankar Bandyopadhyay
• 金额: $ 9.32万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-04-04 至 2013-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8319854
• 关键词: Accounting; Address; Admixture; Adult; Affect; African American; Aftercare; Age; Apical; Basic Science; Behavioral; Biological; Centers of Research Excellence; Characteristics; Chronic; Clinical; Communities; Complex; Consensus; Data; Data Set; Databases; Dental; Dental Research; Disease; Disease Progression; Enrollment; Epithelial Attachment; Equilibrium; Evaluation; Genetic; Gingivitis; Goals; Habits; Health; Health Professional; Health Status; Hemorrhage; Inflammatory; Joints; Judgment; Knowledge; Left; Literature; Location; Markov Chains; Measures; Medical; Methodology; Methods; Modeling; Monitor; National Health and Nutrition Examination Survey; National Institute of Dental and Craniofacial Research; Normalcy; Oral; Oral Examination; Oral cavity; Oral health; Outcome; Pattern; Performance; Periodontal Diseases; Periodontal Ligament; Periodontal Pocket; Periodontitis; Population; Poverty; Prevention strategy; Public Health; Research Design; Research Personnel; Risk Assessment; Science; Selection Criteria; Site; Smoking; South Carolina; Specific qualifier value; Staging; Statistical Methods; Statistical Models; Strategic Planning; Techniques; Tooth Exfoliation; Tooth Loss; Tooth structure; Universities; Ursidae Family; alveolar bone; base; craniofacial; demographics; diabetic; disorder control; early experience; improved; interest; migration; novel; prevent; response; simulation; time interval; tool; treatment planning; user friendly software

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.

描述（由申请人提供）：牙周炎是一种牙周组织的慢性炎症性疾病，估计影响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、社会人口学、行为学和其他医学因素。然而，用来理解这些的统计方法带来了一些有趣的挑战。数据是多变量的、非高斯的、非平稳的，并且具有缺失的信息，这些信息是该口腔区域的口腔健康状态的信息。此外，可以推测牙周进展可以空间参考。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，拟议的研究将开发强大的统计方法，结合所有上述挑战，用于评估牙周病状态和识别相关的重要协变量。主题：将在纳入Gullah非裔美国人（AA）糖尿病患者（GAAD）研究（作为SC COBRE口腔健康研究的一部分）的313名齿状受试者的数据集上评价统计方法。为便于推广，将对作为国家卫生和保健调查（1999-2004年）一部分收集的全国代表性数据进行调查。可用数据和研究设计：作为GAAD研究的一部分，在南卡罗来纳州医科大学（MUSC）收集了牙周病状态（由研究中心级囊袋部门、临床附着水平和探查出血确定）、其他相关生物学和医学状态，如吸烟习惯、刷牙和使用牙线习惯、人口统计学（贫困状态）和其他参数。Gullah-AA受试者代表了一个有趣的人群，具有最小的遗传混合物，其牙齿健康状况仍然非常未知。NHANES数据是公开的。重要性：新的统计方法将为牙科研究人员提供有关空间关联如何在牙周数据的上述典型特征存在下预测牙周进展的增强知识。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。