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

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.