NOVEL STATISTICAL MODELS FOR DENTAL CARIES

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中获得的龋齿数据进行详细的统计检查。在此过程中，将开发新的统计模型，用于单变量和双变量计数数据，包括纵向，以及横截面和边缘，无论是在人的水平和牙齿水平。 在美国，龋齿这是一种主要的儿童慢性疾病。然而，我们对各种风险因素的了解有限。爱荷华州氟化物研究是一项正在进行的研究，对爱荷华州儿童的队列，开始于1991年，由史蒂文利维博士领导，他是这项申请的共同研究者。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得数据，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齿和氟中毒的双变量计数数据进行联合建模，重新调查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，因为两个边际区间的氟使用对龋齿和氟中毒的预防没有重叠的受试者对受试者的变异性。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龋病发病率和严重程度的各种（危险）因素的动态变化，包括有效的参数估计计算方法（目标1）。我们将开发各种边际模型，以了解龋病发病率和严重程度与适用于整个人群和评价期的各种因素之间的关系（目标2）。我们将开发双变量计数数据回归模型，以研究在可比评估（目标3）龋齿和氟中毒的联合关系。我们将比较我们的结果从现有的方法。新数据分析方法的统计软件（R包/代码）将通过Comprehensive R Archive Network免费分发。

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.