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

摘要 我们建议开发新的贝叶斯模型，包括联合模型，用于纵向数据， 集群和非连续（更具体地说，计数和有序）。利用这些新开发的 模型，我们将对总积累进行全面和精细的统计检查， 从爱荷华州氟化物研究参与者获得的龋齿和氟中毒数据。因此电流 该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相匹配， 9、13、17和23岁，这是爱荷华州儿童队列研究的参与者。的 总体目标将是研究各种风险的时变（特别是长期）和联合效应 龋齿和氟中毒结果的保护因素。 爱荷华州氟化物研究（IFS）是从爱荷华州队列中获得的有价值信息的独特数据源 这个项目始于1991年，由史蒂文·利维博士领导，他是这个项目的共同作者。这些丰富而 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，在儿童，青少年和成年早期。除了龋齿和氟中毒的分数， 数据集包含一些重要的支持变量的信息，包括氟化物，钙， 含糖饮料摄入量，可用作统计模型中的解释变量。成果 测量是非高斯的（计数和顺序），不同牙齿，表面和区域的数据 由于刷牙行为等各种共享因素，给定个体是相关的; 此外，相关性本质上是时空的。总的来说，现成的统计方法是 我们无法完全理解这些数据。借助我们的合作经验， 在早期的R 03中，IFS数据的先前方面，我们计划在更大的范围内进行调查。 综合水平。特别是，纳入23岁时的数据时，参与者达到早期 从科学和统计建模的角度来看，成年都是重要的。此外，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，龋齿和氟中毒结果的联合模型的开发， 预测分布和缺失数据成分的处理将是本研究的重要新特征。 目前的提案。 因此，将实现以下两个相继的目标。我们将开发一个新的纵向计数 数据回归模型，并使用它来分析龋齿数据在5岁，9岁，13岁，17岁，和23岁（目标1a）。旁边， 我们将建立一个新的纵向有序数据回归模型，并将其用于分析氟中毒数据 在9岁、13岁、17岁和23岁时（目标1b）。最后，我们将开发一个联合纵向模型，当一个响应 分量为计数和其他序数，并将其用于龋病和氟中毒数据的联合获取 更有效的统计估计，并建立预测模型，为未来的结果， 儿童的协变量特征（目标2）。 有效的贝叶斯计算算法将开发这些目标。我们将比较 我们的结果与现有方法（无论何时存在）获得的结果以及 现有的龋齿和氟中毒文献。统计软件（OpenBUGS、STAN和/或R 软件包/代码）实现时间聚类计数数据和顺序分析方法将被 通过PI的网站和Comprehensive R Archive Network免费分发。

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.