Longitudinal Analysis of Iowa Fluoride Study Data, Including at Age Twenty-three

爱荷华州氟化物研究数据的纵向分析，包括 23 岁的数据

• 批准号: 10551892
• 负责人: Somnath Datta
• 金额: $ 15.22万
• 依托单位: UNIVERSITY OF FLORIDA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-01 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10551892
• 关键词: Acquired Dental Fluorosis; Adolescence; Age; Algorithms; Archives; Bayesian Analysis; Bayesian Modeling; Behavior; Beverages; Biometry; Calcium; Child; Childhood; Code; Cohort Studies; Collaborations; Complex; Computer software; Computing Methodologies; Data; Data Set; Data Sources; Dental caries; Development; Fluorides; Future; Goals; Individual; Intake; Investigation; Iowa; Joints; Literature; Long-Term Effects; Measurable; Methodology; Methods; Modeling; Nature; Oral health; Outcome; Outcome Measure; Participant; Pattern; Preventive; Recording of previous events; Regression Analysis; Research; Research Methodology; Risk; Risk Factors; Source; Statistical Methods; Statistical Models; Structure; Surface; Techniques; Time; Time Study; Tooth structure; Toothbrushing; Work; cohort; complex data; emerging adult; experience; fluorosis; improved; insight; longitudinal analysis; novel; predictive modeling; protective factors; response; spatiotemporal; technique development; temporal measurement; web site

Abstract We propose to develop novel Bayesian models, including joint models, for longitudinal data that are clustered and non-continuous (more specifically, count and ordinal). Using these newly developed models, we will undertake a comprehensive and refined statistical examination of the total accumulation of dental caries and fluorosis data obtained from Iowa Fluoride Study participants. Thus, the current project will fit longitudinal statistical models to caries and fluorosis scores data obtained at ages five, nine, thirteen, seventeen, and twenty-three, for the participants in this cohort study of Iowa children. The overall goal will be to study the time-varying (in particular, long-term) and joint effects of various risk and protective factors for dental caries and fluorosis outcomes. Iowa Fluoride Study (IFS) is a unique data source of valuable information resulting from a cohort of Iowa children that began in 1991, led by Dr. Steven Levy, who is a co-I on this proposal. These rich and complex data allow development of models to study two important oral health conditions, caries and fluorosis, in childhood, adolescence, and early adulthood. Besides the caries and fluorosis scores, this dataset has information on a number of important supporting variables, including fluoride, calcium, and sugared-beverage intakes which can be used as explanatory variables in statistical models. The outcome measures are non-Gaussian (count and ordinal), and the data on different teeth, surfaces, and zones of a given individual are correlated due to various shared factors such as toothbrushing behaviors; additionally, the correlations are spatio-temporal in nature. Overall, off-the-shelf statistical methods are not able to provide a full understanding of these data. Aided by our collaborative experiences analyzing previous aspects of IFS data in earlier R03s, we plan to undertake our investigation at a more comprehensive level. In particular, incorporation of data at age 23 when participants reached early adulthood will be significant both from scientific and statistical modeling standpoints. In addition, novel examination of the best choices of the covariate information, the random effects structure leading to spatio-temporal correlations, development of a joint model for caries and fluorosis outcomes, and their predictive distributions, and handling of missing data components will be important novel features of this current proposal. Thus, the following two sequential aims will be undertaken. We will develop a new longitudinal count data regression model and use it to analyze the caries data at ages 5, 9, 13, 17, and 23 (Aim 1a). Alongside, we will develop a new longitudinal ordinal data regression model and use it to analyze the fluorosis data at ages 9, 13, 17, and 23 (Aim 1b). Finally, we will develop a joint longitudinal model when one response component is count and the other ordinal, and use it for the caries and fluorosis data together to obtain more statistically efficient estimators and to establish predictive models for future outcomes given the covariate profiles of a child (Aim 2). Algorithms for efficient Bayesian computation will be developed for each of these aims. We will compare our results to those obtained from existing approaches (whenever they exist) and also results available in the existing caries and fluorosis literature. Statistical software (OpenBUGS, STAN and/or R packages/codes) implementing the temporal clustered count data and ordinal analysis methods will be freely distributed through the PI's web-site and through the Comprehensive R Archive Network.

抽象的 我们建议为纵向数据开发新颖的贝叶斯模型，包括联合模型 集群和非连续（更具体地说，计数和序数）。利用这些新开发的 模型，我们将对累计总量进行全面、精细的统计检验 从爱荷华州氟化物研究参与者获得的龋齿和氟中毒数据。因此，当前 项目将把纵向统计模型与五岁时获得的龋齿和氟中毒评分数据进行拟合， 九、十三、十七和二十三，分别为爱荷华州儿童队列研究的参与者。这 总体目标是研究各种风险的时变（特别是长期）和联合影响 以及龋齿和氟中毒结果的保护因素。 爱荷华州氟化物研究 (IFS) 是爱荷华州队列中有价值信息的独特数据源 该项目始于 1991 年，由 Steven Levy 博士领导，他是该提案的共同参与人之一。这些富人和 复杂的数据允许开发模型来研究两种重要的口腔健康状况：龋齿和 氟中毒，发生在儿童期、青春期和成年早期。除了龋齿和氟中毒评分之外，这 数据集包含许多重要支持变量的信息，包括氟化物、钙和 含糖饮料摄入量可用作统计模型中的解释变量。结果 度量是非高斯的（计数和序数），并且不同牙齿、表面和区域的数据 由于各种共同因素（例如刷牙行为），给定个体之间存在相关性； 此外，这些相关性本质上是时空的。总体而言，现成的统计方法是 无法提供对这些数据的全面理解。借助我们的协作经验分析 早期 R03 中的 IFS 数据的先前方面，我们计划在更进一步的情况下进行调查 综合水平。特别是，纳入 23 岁时的数据，当时参与者很早就到达了 从科学和统计模型的角度来看，成年期都具有重要意义。另外，小说 检查协变量信息的最佳选择，随机效应结构导致 时空相关性、龋齿和氟中毒结果联合模型的开发及其 预测分布和丢失数据组件的处理将是该方法的重要新特征 当前的提案。 因此，将实现以下两个连续目标。我们将开发一种新的纵向计数 数据回归模型，并用它来分析 5、9、13、17 和 23 岁的龋齿数据（目标 1a）。旁边， 我们将开发一个新的纵向序数数据回归模型并用它来分析氟中毒数据 9、13、17 和 23 岁（目标 1b）。最后，当一个响应时，我们将开发一个联合纵向模型 一个分量是计数，另一个是序数，将其与龋齿和氟中毒数据一起使用以获得 统计上更有效的估计器，并根据给定的情况建立未来结果的预测模型 儿童的协变量概况（目标 2）。 将为每个目标开发有效贝叶斯计算的算法。我们将比较 我们的结果与从现有方法（无论何时存在）获得的结果以及可用的结果 现有有关龋齿和氟中毒的文献。统计软件（OpenBUGS、STAN 和/或 R 包/代码）实现时间聚类计数数据和序数分析方法将是 通过 PI 网站和综合 R 档案网络免费分发。