Exploring tooth survival using Bayesian spatial models

使用贝叶斯空间模型探索牙齿存活率

• 批准号: 8699584
• 负责人: Dipankar Bandyopadhyay
• 金额: $ 17.31万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-04-01 至 2016-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8699584
• 关键词: Accounting; Acute; Address; Adult; Advanced Development; Age; Area; Basic Science; Bayesian Modeling; Centers for Disease Control and Prevention (U.S.); Clinical; Clinical Research; Complex; Data; Data Set; Data Sources; Databases; Dental; Dental General Practice; Dental Hygiene; Dental Insurance; Dental caries; Dependence; Development; Diabetes Mellitus; Disease; Disease Marker; Disease Progression; Equilibrium; Evaluation; Event; Exhibits; Future; Gender; Goals; Health; Judgment; Knowledge; Lead; Literature; Location; Longevity; Longitudinal Studies; Measures; Medical; Methodology; Methods; Modeling; Monitor; Oral cavity; Pain; Patients; Performance; Periodontal Diseases; Population; Probability; Prognostic Marker; Public Health; Quality of life; Race; Records; Research; Research Design; Research Personnel; Risk; Risk Assessment; Risk Estimate; Risk Factors; Selection Criteria; Smoking Status; Statistical Methods; Statistical Models; Structure; Survival Analysis; System; Testing; Time; Tooth Diseases; Tooth Loss; Tooth structure; Universities; Vision; cost; cost effective; demographics; disability; disorder prevention; effective therapy; evidence base; experience; follow-up; hazard; interest; longitudinal database; novel; novel strategies; outcome forecast; public health relevance; response; simulation; time use; treatment planning; user friendly software

DESCRIPTION (provided by applicant): Exploring tooth survival using Bayesian spatial models Caries and severe periodontal disease eventually lead to tooth loss, and this remains a major public health burden in the US. Future dental treatment plans will benefit from development of advanced statistical methods to integrate efficient risk assessment and short-term prediction of tooth loss. Dental datasets come with many interesting statistical challenges which severely limit the potential of currently available methods. In addition to tooth-within-mouth clustering, the times to events are spatially dependent, non-stationary (varying with tooth-locations), and experience heavy censoring. These factors also complicate the interpretation of clinical findings, which are needed at the conditional (subject-level) and the marginal (population) levels. Currently available statistical methods might handle some, but not all of these within an unified paradigm. Goals: Using a Bayesian framework, the proposed study will assess and monitor dental disease status of a population of interest and identify covariates associated with tooth- loss leading to efficient short-term prediction. Subjects: The statistical methods will be initially evaluated on a dataset of about 100 dentate subjects from the McGuire and Nunn data who were monitored at a private dental practice in the Houston area for about 16 years. For generalizability, the methods will be tested on a 4-year longitudinal database consisting of about 16,500 patients collected at Creighton University. Study design: A clustered-longitudinal study design with time to event endpoint comprises the databases that recorded age, gender, race, complete restorative and periodontal records with follow-up, smoking status, diabetes status, oral hygiene, and other essential parameters. Significance: The current project will provide new knowledge to unravel the complex covariate-response relationship that determines tooth loss, and can be easily generalized to other dental datasets. The long-term goal is to be able to achieve accurate predictive inference on tooth survival enabling dental practitioners to develop cost-effective dental treatment plans.

描述（由申请人提供）：使用贝叶斯空间模型龋齿和严重牙周疾病探索牙齿生存，最终导致牙齿脱落，这仍然是美国的主要公共卫生负担。未来的牙科治疗计划将受益于高级统计方法的开发，以整合有效的风险评估和短期牙齿脱落的预测。牙科数据集带来了许多有趣的统计挑战，这些挑战严重限制了当前可用方法的潜力。除了口腔内聚类外，事件的时代在空间依赖，非平稳（随牙齿分离而变化），并经历了重度检查。这些因素也使对条件（主题级）和边际（人口）水平所需的临床发现的解释变得复杂。当前可用的统计方法可能会处理一些，但并非全部在统一范式中。目标：使用贝叶斯框架，拟议的研究将评估和监测感兴趣的人群的牙齿疾病状况，并确定与牙齿损失相关的协变量，从而导致有效的短期预测。受试者：统计方法最初将在McGuire和Nunn数据的大约100名牙齿受试者的数据集上进行评估，这些数据在休斯顿地区的私人牙科实践中进行了监测约16年。为了概括性，该方法将在4年的纵向数据库中进行测试，该数据库由Creighton University收集的约16,500名患者组成。研究设计：一个群集的纵向研究设计，其时间为事件终点的时间包括记录年龄，性别，种族，完整恢复性和牙周记录的数据库，具有随访，吸烟状态，糖尿病状态，口腔卫生和其他必要参数。意义：当前的项目将提供新的知识，以揭示决定牙齿脱落的复杂协变量 - 响应关系，并且很容易将其推广到其他牙科数据集。长期目标是能够对牙齿存活获得准确的预测推断，从而使牙科从业人员能够制定具有成本效益的牙科治疗计划。