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

基本信息

批准号：
8096813
负责人：
Dipankar Bandyopadhyay
金额：
$ 1.62万
依托单位：
MEDICAL UNIVERSITY OF SOUTH CAROLINA
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-07-01 至 2011-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8096813
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 Normalcy 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

项目摘要

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）：美国的大多数成年人受牙周疾病或龋齿引起的牙齿脱水的影响。通过专业牙科治疗和个人口腔健康自我保健来预防牙齿脱落，通过保持舒适性和功能状态的自然牙列。为了制定适当的牙科治疗计划，牙科医疗保健专业人员必须了解可能影响牙周疾病或牙科龋齿状态确定的最有效的生物学，社会人口统计学，行为和其他医疗因素。但是，目前尚无关于可能影响牙齿疾病的最重要因素的共识，也没有确定这些因素的最佳统计方法。在适用于牙周疾病和牙科症状结局的可变统计模型中，需要可变选择方法，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：拟议的研究将开发固定效应（协变量）和随机效应选择技术，用于通过聚类引起的潜在随机效应的多变量牙科数据进行选择，并将这些方法应用于可用数据库，以记录牙科健康状况，以提高有关与牙齿损失相关的因素的知识。受试者：将在300名牙齿受试者的数据集上评估统计方法，这些牙齿受试者被纳入了Gullah African-American（AA）糖尿病患者研究，作为SC Cobre的口腔健康状况的一部分。为了普遍性，将对作为NHANES一部分收集的国家数据进行研究（1999-2004）。可用的数据和研究设计：牙周状态（由口袋深度和临床依恋水平确定），龋齿状态（由牙齿水平DMFS指数确定），其他相关的生物学/医疗状况，吸烟，行为，行为（刷牙和牙线），人口统计学（贫困状态）和其他参数已在南卡罗来纳州的医学院收集。 Gullah AA的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：新的统计方法将通过为牙科研究人员提供对协变量与牙齿健康之间关联性质的知识来提高公共卫生，并且更广泛地使研究人员能够更好地针对风险评估和预防策略，从而提高健康状况。公共卫生相关性：牙科官员之间缺乏共识来选择可能影响龋齿和牙周疾病确定的牙齿脱落的最重要的协变量。我们提出的强大统计方法将通过特定的应用来解决此问题，以探讨说话的非洲裔美国人的牙齿健康状况，以及作为NHANES（1999-2004）的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，长期目标是为牙科研究人员（以及其他健康科学家）对重复和纵向牙齿（健康）数据有更好的了解，以预防和控制疾病并改善牙齿健康。