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NOVEL STATISTICAL MODELS FOR DENTAL CARIES

龋齿的新颖统计模型

基本信息

批准号：
8485583
负责人：
Somnath Datta
金额：
$ 13.72万
依托单位：
UNIVERSITY OF LOUISVILLE
依托单位国家：
美国
项目类别：
财政年份：
2012
资助国家：
美国
起止时间：
2012-07-01 至 2015-06-30
项目状态：
已结题

项目摘要

DESCRIPTION (provided by applicant): The overall goal of this application is to undertake a detailed statistical examination of caries data obtained from the Iowa Fluoride Study. In this process, novel statistical models will be developed for univariate and bivariate count data both longitudinally, as well as, cross-sectionally and marginally, both at person level and tooth level. In the United States, dental caries this is a major chronic childhood disease. Nevertheless, our understanding of various risk factors is limited. The Iowa Fluoride Study is an ongoing study on a cohort of Iowa children that began in 1991, led by Dr. Steven Levy who is a co-I on this application. It is anticipated that through innovative and efficient statistical modeling, it will e possible to mine the resulting data more fully and discover novel relationships between caries incidences/severity and various potential risk factors. We also plan to reinvestigate the issue of optimal fluoride use through joint modeling of bivariate count data of caries and fluorosis. Prior analysis using univariate approaches indicated that a recommendation of optimal dosage is problematic since the two marginal intervals for fluoride use towards caries and fluorosis prevention did not overlap on the face of subject to subject variability. Thus, the following three broad interconnected aims will be undertaken. We will develop count data models to study the dynamical changes of the various (risk) factors on caries incidence and severity including efficient computational methods for parameter estimation (Aim 1). We will develop various marginal models to understand the relationship between caries incidences and severity with various factors that are applicable across the population and evaluation periods (Aim 2). We will develop bivariate count data regression models to study the joint relationships of caries and fluorosis at comparable evaluations (Aim 3). We will compare our results to those obtained from existing approaches. Statistical software (R packages/codes) for the novel data analysis methods will be freely distributed through the Comprehensive R Archive Network.

描述(由申请人提供)：本申请的总体目标是对爱荷华州氟化物研究获得的龋齿数据进行详细的统计检查。在这一过程中，将开发新的统计模型，既包括纵向的单变量和双变量计数数据，也包括人水平和牙齿水平的横截面和边缘水平的统计模型。在美国，龋齿是儿童时期的一种主要慢性疾病。然而，我们对各种风险因素的了解是有限的。爱荷华州氟化物研究是一项对爱荷华州儿童队列的持续研究，始于1991年，由史蒂文·利维博士领导，他是这项应用的共同研究人员。预计通过创新和有效的统计建模，将有可能更充分地挖掘所产生的数据，并发现龋齿发病率/严重程度与各种潜在风险因素之间的新关系。我们还计划通过对龋病和氟骨症的双变量计数数据进行联合建模，重新研究最佳氟化物使用的问题。先前使用单变量方法的分析表明，最佳剂量的建议是有问题的，因为氟化物用于龋齿和氟中毒预防的两个边际间隔在受试者的脸上并不重叠，受试者的变异性。因此，以下三个将实现广泛的相互关联的目标。我们将开发计数数据模型来研究影响龋病发病率和严重程度的各种(危险)因素的动态变化，包括用于参数估计的有效计算方法(目标1)。我们将开发各种边际模型，以了解龋齿发病率和严重程度与各种因素之间的关系，这些因素适用于整个人群和评估期(目标2)。我们将建立双变量计数数据回归模型，在可比评估中研究龋病和氟骨症的联合关系(目标3)。我们将把我们的结果与现有方法得到的结果进行比较。用于新数据分析方法的统计软件(R包/代码)将通过全面R档案网络免费分发。