Robust nonparametric methods with variable selection for clustered dental data

描述(申请人提供)：在美国，大多数成年人都会因为牙周病或龋齿而失去牙齿。防止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、社会人口、行为和其他医学因素，这些因素可能会影响牙周病或龋齿状况所决定的牙齿脱落。然而，目前，对于可能影响牙病的最重要的因素，以及识别这些因素的最佳统计方法，还没有达成共识。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集性(即每个受试者的多个结果)。目标：这项拟议的研究将开发用于多变量牙科数据的固定效应(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的建模，并将这些方法应用于记录牙齿健康状况的现有数据库，以促进对与牙齿脱落相关因素的了解。受试者：统计方法将在300名齿状受试者的数据集上进行评估，这些受试者是作为SC Cobre口腔健康研究的一部分参加Gullah非洲裔美国人(AA)糖尿病研究的。为便于推广，将对作为国民健康和经济分析系统的一部分(1999-2004年)收集的国家数据进行调查。可获得的数据和研究设计：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)、其他相关的生物/医疗状况、吸烟、行为(刷牙和牙线)、人口统计学(贫困状况)和其他参数。古拉AA受试者代表了一个有趣的人群，他们的牙齿健康状况仍然非常未知，基因混合体最少。NHANES的数据是公开的。意义：新的统计方法将通过向牙科研究人员提供关于协变量和牙齿健康之间关联的性质的更多知识来促进公共健康，更广泛地说，通过使研究人员能够更有针对性地进行风险评估和预防策略，从而改善健康状况。 公共卫生相关性：牙科卫生学家在选择可能影响龋齿和牙周病决定的牙齿脱落的最重要的协变量方面缺乏共识。我们提出的稳健的统计方法将解决这一问题，具体应用于探索讲古拉语的非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众健康产生深远影响，我们的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，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.