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，用于以下纵向数据 成簇的和非连续的(更具体地说，计数和序数)。使用这些新开发的 模型，我们将对总积累进行全面和细化的统计检查 从爱荷华州氟化物研究参与者那里获得的龋齿和氟斑牙数据。因此，当前的 该项目将使纵向统计模型与五岁时获得的龋齿和氟斑牙评分数据相匹配， 对于这项爱荷华州儿童队列研究的参与者来说，是9、13、17和23。这个 总体目标将是研究各种风险的时变性(特别是长期)和联合影响。 以及龋齿和氟中毒后果的保护因素。 爱荷华州氟化物研究(IFS)是爱荷华州一群有价值信息的独特数据来源 儿童项目始于1991年，由史蒂文·利维博士领导，他是这项提案的共同倡导者。这些富有的和 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，即龋齿和 儿童、青春期和成年早期的氟中毒。除了龋齿和氟骨症分数，这一点 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，可作为统计模型中的解释变量。结果是 测量是非高斯的(计数和序数)，并且关于不同牙齿、表面和区域的数据 由于刷牙行为等各种共同因素，特定的个体是相关的； 此外，这种相关性在本质上是时空的。总的来说，现成的统计方法有 不能提供对这些数据的完全理解。借助我们的协作经验分析 在更早的R03中，我们计划在更多的地方进行我们的调查 综合水平。特别是，当参与者在23岁时很早就到达时，纳入数据 从科学和统计建模的角度来看，成年都将具有重要意义。此外，小说 检验协变量信息的最佳选择，随机效应结构导致 龋病和氟中毒结果的时空相关性、联合模型的发展及其相互关系 预测分布和对丢失数据分量的处理将是其重要的新特性 目前的建议。 因此，将实现以下两个相继目标。我们将开发一种新的纵向计数 数据回归模型，并用它分析5、9、13、17和23岁的龋病数据(目标1a)。在此同时， 我们将开发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(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COUNT和其他序数，并将其用于龋病和氟中毒数据，以获得 在统计上更有效的估计者，并建立对未来结果的预测模型 儿童的协变量特征(目标2)。 将为每个目标开发有效的贝叶斯计算算法。我们会比较一下 我们的结果是从现有方法获得的结果(只要存在)，也可以在 现有的龋病和氟骨症文献。统计软件(OpenBUGS、STAN和/或R 包/代码)将实现时间聚集计数数据和序数分析方法 通过PI的网站和全面R档案网免费分发。

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.