Exploring tooth survival using Bayesian spatial models

使用贝叶斯空间模型探索牙齿存活率

基本信息

批准号：
9195676
负责人：
Dipankar Bandyopadhyay
金额：
$ 8.69万
依托单位：
VIRGINIA COMMONWEALTH UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-04-01 至 2018-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9195676
关键词：
Exploring tooth survival using Bayesian

项目摘要

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探索牙齿存活情况龋齿和严重的牙周病最终导致牙齿脱落，这仍然是美国主要的公共卫生负担。未来的牙科治疗计划将受益于先进统计方法的发展，以整合有效的风险评估和牙齿缺失的短期预测。牙科数据集面临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。除了口内牙齿聚类之外，事件发生的时间具有空间依赖性、非平稳性（随牙齿位置而变化），并且经历严格的审查。这些因素也使临床结果的解释变得复杂，而临床结果是条件（受试者水平）和边缘（人群）水平所需要的。当前可用的统计方法可以在统一的范例中处理一些，但不是全部。目标：使用贝叶斯框架，拟议的研究将评估和监测感兴趣人群的牙科疾病状况，并识别与牙齿缺失相关的协变量，从而实现有效的短期预测。受试者：统计方法将在来自 McGuire 和 Nunn 数据的约 100 名有齿受试者的数据集上进行初步评估，这些受试者在休斯顿地区的一家私人牙科诊所接受了约 16 年的监测。为了具有普遍性，这些方法将在一个为期 4 年的纵向数据库上进行测试，该数据库由克赖顿大学收集的约 16,500 名患者组成。研究设计：具有事件终点时间的集群纵向研究设计包括记录年龄、性别、种族、完整的修复和牙周记录以及随访、吸烟状况、糖尿病状况、口腔卫生和其他基本参数的数据库。意义：当前项目将提供新知识来阐明决定牙齿缺失的复杂协变量响应关系，并且可以轻松推广到其他牙科数据集。长期目标是能够对牙齿存活率进行准确的预测推断，使牙科医生能够制定具有成本效益的牙科治疗计划。