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: 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
• 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

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.

项目成果

Nonparametric spatial models for clustered ordered periodontal data.

• DOI: 10.1111/rssc.12150
• 发表时间: 2016-08
• 期刊: Journal of the Royal Statistical Society. Series C, Applied statistics
• 作者: Bandyopadhyay D;Canale A
• 通讯作者: Canale A

Quantile regression in linear mixed models: a stochastic approximation EM approach.

• DOI: 10.4310/sii.2017.v10.n3.a10
• 发表时间: 2017
• 期刊: Statistics and its interface
• 影响因子: 0.8
• 作者: Galarza CE;Lachos VH;Bandyopadhyay D
• 通讯作者: Bandyopadhyay D

A partially linear additive model for clustered proportion data.

• DOI: 10.1002/sim.7573
• 发表时间: 2018-03-15
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Zhao W;Lian H;Bandyopadhyay D
• 通讯作者: Bandyopadhyay D

Comparing conditional survival functions with missing population marks in a competing risks model.

• DOI: 10.1016/j.csda.2015.10.001
• 发表时间: 2016-03-01
• 期刊: Computational statistics & data analysis
• 影响因子: 1.8
• 作者: Bandyopadhyay D;Jácome MA
• 通讯作者: Jácome MA