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）：牙周炎是一种牙周炎性疾病，影响35岁以上的美国成年人中有50％。要制定适当的治疗计划和治疗计划，牙科保健专业人员必须了解生物，社会人口统计学，行为，行为，行为和其他医疗因素，从而影响牙周牙周进行牙齿的牙齿丧失。但是，用于理解这些方法的统计方法带来了一些有趣的挑战。数据是多元，非高斯，非固定的，并且缺少信息，这些信息详细介绍了该口腔区域的口腔健康状况。此外，可以猜想可以在空间上引用牙周进展。当前的统计方法并未在统一框架下解决所有这些方法。目标：使用贝叶斯范式，拟议的研究将开发出强大的统计方法，结合上述所有挑战，以评估牙周疾病状况并确定相关的重要协变量。受试者：将在313名牙齿受试者的数据集中评估统计方法，这些牙齿受试者已入学，作为口腔健康的SC Cobre的一部分。为了概括性，将对作为NHANES（1999-2004）一部分收集的全国代表性数据进行研究。可用的数据和研究设计：牙周状态（由现场水平的口袋部，临床依恋水平以及探测时出血），其他相关的生物学和医学状况，例如吸烟习惯，刷牙和牙线习惯，人口统计学（贫困状态）和其他参数，以及其他参数，以及南卡罗来纳州医学院（MUSC）（MUSC）（MUSC）（MUSC）的一部分。 Gullah-AA受试者代表了一个有趣的人群，其遗传混合物的牙齿健康状况仍然鲜为人知。 NHANES数据公开可用。意义：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存在上述牙周数据典型特征的情况下如何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策略，从而改善健康状况。