Robust nonparametric methods with variable selection for clustered dental data

具有聚类牙科数据变量选择的稳健非参数方法

• 批准号: 7991211
• 负责人: Dipankar Bandyopadhyay
• 金额: $ 15.56万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7991211
• 关键词: Accounting; Address; Admixture; Adult; Affect; African American; Age; American; Behavioral; Biological; Centers of Research Excellence; Clinical; Clinical Research; Cluster Analysis; Communicable Diseases; Communities; Consensus; Data; Data Analyses; Data Set; Databases; Dental; Dental Research; Dental caries; Dentition; Diagnosis; Disease; Disease Progression; Enrollment; Equation; Equilibrium; Evaluation; Family; Genetic; Goals; Health; Health Professional; Health Status; Hemorrhage; Heterogeneity; Individual; Inflammation; Judgment; Knowledge; Lead; Literature; Markov Chains; Mastication; Measures; Mediating; Medical; Methodology; Methods; Modeling; National Health and Nutrition Examination Survey; National Institute of Dental and Craniofacial Research; Nature; Oral cavity; Oral health; Outcome; Pain; Performance; Periodontal Diseases; Periodontitis; Population; Poverty; Prevention; Prevention strategy; Public Health; Research; Research Design; Research Personnel; Risk Assessment; Sampling; Scientist; Selection Criteria; Self Care; Smoking Status; South Carolina; Specific qualifier value; Staging; Statistical Methods; Statistical Models; Structure; Subjects Selections; Surface; Techniques; Tooth Diseases; Tooth Loss; Tooth structure; Universities; Weight; Work; base; bone; diabetic; disorder control; flexibility; improved; indexing; innovation; insight; interest; oral biofilm; permanent tooth; prevent; public health relevance; response; simulation; soft tissue; time interval; tool; tooth surface; treatment planning; user friendly software

DESCRIPTION (provided by applicant): Most adults in the US are affected by tooth loss due to periodontal disease or dental caries. Prevention of tooth loss is achieved through professional dental treatment and personal oral health self-care by maintaining the natural dentition in a state of comfort and function. In order to develop appropriate dental treatment planning, dental health care professionals must understand the most effective biological, socio-demographic, behavioral and other medical factors that can affect tooth loss as determined by periodontal disease or dental caries status. Currently, however, there is no consensus concerning the most important factors that may influence dental disease, nor the optimal statistical methods for identifying these factors. There is a need for variable selection methods in robust statistical models for periodontal disease and dental caries outcomes that accommodate the clustered nature of these data (i.e. multiple outcomes from each subject). Goals: The proposed study will develop fixed effects (covariates) and random effects selection techniques for multivariate dental data with robust modeling of the latent random effects induced by clustering and will apply these methods to available databases recording dental health status to advance knowledge about factors associated with tooth loss. Subjects: The statistical methods will be evaluated on a dataset of 300 dentate subjects who were enrolled in the Gullah African-American (AA) Diabetics Study as part of the SC COBRE for Oral Health. For generalizability, the methods will be investigated on national data collected as part of NHANES (1999- 2004). Available data and study design: Periodontal status (determined by pocket depth and clinical attachment level), caries status (determined by tooth level DMFS index), other relevant biological/medical status, smoking, behavioral (brushing and flossing), demographic (poverty status) and other parameters have been collected at the Medical University of South Carolina. 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 advance public health by providing dental researchers enhanced knowledge about the nature of the associations between covariates and dental health and, more broadly, by enabling researchers to better target risk assessment and prevention strategies, thereby improving health status. PUBLIC HEALTH RELEVANCE: There is a lack of consensus among dental hygenists to select the most important covariables that might influence tooth loss as determined by caries and periodontal disease. Our proposed robust statistical methods will address this issue with specific applications to explore the dental health status of Gullah-speaking African- Americans, as well as national data collected as part of NHANES (1999-2004). Our methods will have a profound impact on overall public health and the long term goal is to provide dental researchers (as well as other health scientists) a better understanding about repeated and longitudinal dental (health) data so as to prevent and control disease and improve dental health.

描述（由申请人提供）：美国大多数成年人都因牙周病或龋齿而受到牙齿脱落的影响。预防牙齿脱落是通过专业的牙科治疗和个人口腔健康自我护理，保持天然牙列处于舒适和功能状态来实现的。为了制定适当的牙科治疗计划，牙科保健专业人员必须了解根据牙周病或龋齿状况确定的可能影响牙齿脱落的最有效的生物学、社会人口、行为和其他医学因素。然而，目前对于可能影响牙科疾病的最重要因素以及识别这些因素的最佳统计方法尚未达成共识。牙周病和龋齿结果的稳健统计模型中需要变量选择方法，以适应这些数据的聚类性质（即每个受试者的多个结果）。目标：拟议的研究将为多元牙科数据开发固定效应（协变量）和随机效应选择技术，并对聚类引起的潜在随机效应进行稳健建模，并将这些方法应用于记录牙齿健康状况的可用数据库，以增进对与牙齿缺失相关因素的了解。受试者：统计方法将在 300 名有齿受试者的数据集上进行评估，这些受试者参加了 Gullah 非裔美国人 (AA) 糖尿病研究，作为口腔健康 SC COBRE 的一部分。为了具有普遍性，将根据 NHANES（1999-2004 年）收集的国家数据对这些方法进行研究。现有数据和研究设计：南卡罗来纳医科大学收集了牙周状况（由牙周袋深度和临床附着水平决定）、龋齿状况（由牙齿水平 DMFS 指数决定）、其他相关生物/医学状况、吸烟、行为（刷牙和使用牙线）、人口统计（贫困状况）和其他参数。 Gullah AA 受试者代表了一个有趣的群体，其遗传混合物最少，但其牙齿健康状况仍知之甚少。 NHANES 数据是公开的。意义：新的统计方法将通过为牙科研究人员提供关于协变量与牙齿健康之间关联性质的更多知识来促进公共卫生，更广泛地说，使研究人员能够更好地确定风险评估和预防策略，从而改善健康状况。 公共健康相关性：牙科保健员对于选择可能影响龋齿和牙周病确定的牙齿脱落的最重要的协变量缺乏共识。我们提出的稳健统计方法将通过具体应用来解决这个问题，以探索古拉语非裔美国人的牙齿健康状况，以及作为 NHANES（1999-2004）一部分收集的国家数据。我们的方法将对整体公共卫生产生深远的影响，长期目标是让牙科研究人员（以及其他健康科学家）更好地了解重复和纵向的牙科（健康）数据，从而预防和控制疾病并改善牙齿健康。