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从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。在此过程中，新颖的统计模型将用于单变量和双变量计数数据，既纵向又是在人级和牙齿水平上的横截面和边缘的。在美国，龋齿这是一种主要的慢性儿童疾病。然而，我们对各种风险因素的理解是有限的。爱荷华州氟化物研究是一项正在进行的研究，该研究始于1991年的爱荷华州儿童，由史蒂文·列维（Steven Levy）博士领导，后者是该应用程序的一名共同。可以预见的是，通过创新和有效的统计建模，可以更充分地挖掘所得数据，并发现龋齿发生率/严重性和各种潜在风险因素之间的新型关系。我们还计划通过关节建模龋齿和氟中毒的联合建模来重新研究最佳氟化物使用的问题。使用单变量方法的先前分析表明，最佳剂量的建议是有问题的，因为氟化物用于龋齿和预防氟中毒的两个边际间隔并未在受试者面对受试者变异性上重叠。因此，以下三个将实现广泛的互连目标。我们将开发计数数据模型，以研究龋齿发病率和严重程度的各种（风险）因素的动态变化，包括有效的参数估计计算方法（AIM 1）。我们将开发各种边际模型，以了解龋齿发生率和严重性之间的关系与在整个人群和评估期间适用的各种因素（AIM 2）。我们将开发双变量计数数据回归模型，以在可比评估中研究龋齿和氟中毒的联合关系（AIM 3）。我们将将我们的结果与从现有方法中获得的结果进行比较。新的数据分析方法的统计软件（R软件包/代码）将通过综合R档案网络自由分发。