Robust nonparametric methods with variable selection for clustered dental data

描述（由申请人提供）：美国的大多数成年人都受到牙周病或龋齿导致的牙齿脱落的影响。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来实现的，通过保持自然牙列的舒适和功能状态。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最有效的生物学，社会人口统计学，行为和其他医学因素，这些因素可能会影响牙周疾病或龋齿状态所确定的牙齿脱落。然而，目前还没有共识的最重要的因素，可能会影响牙科疾病，也没有最佳的统计方法来确定这些因素。在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：拟议的研究将开发固定效应（协变量）和随机效应选择技术的多变量牙科数据与强大的建模的潜在随机效应引起的聚类，并将这些方法应用于现有的数据库记录牙齿健康状况，以推进知识与牙齿脱落的相关因素。主题：将在纳入Gullah非裔美国人（AA）糖尿病研究（作为SC COBRE口腔健康研究的一部分）的300名齿状受试者的数据集上评价统计方法。为便于推广，将根据作为国家健康和营养状况调查（1999- 2004年）一部分收集的国家数据对这些方法进行调查。可用数据和研究设计：牙周状况（由牙周袋深度和临床附着水平确定）、龋齿状况（由牙齿水平DMFS指数确定）、其他相关生物/医学状况、吸烟、行为（刷牙和使用牙线）、人口统计学（贫困状况）和其他参数已在南卡罗来纳州医科大学收集。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增强知识，更广泛地说，通过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，从而促进公共卫生。 公共卫生相关性：牙科卫生学家之间缺乏共识，以选择最重要的协变量，可能会影响牙齿脱落，确定龋齿和牙周病。我们提出的强大的统计方法将解决这个问题的具体应用，以探讨牙齿健康状况的Gullah讲非洲裔美国人，以及作为NHANES（1999年至2004年）的一部分收集的国家数据。我们的方法将对整体公共健康产生深远的影响，长期目标是为牙科研究人员（以及其他健康科学家）提供更好地了解重复和纵向牙科（健康）数据，以预防和控制疾病并改善牙齿健康。

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.