The Iowa Fluoride Study has been instrumental in helping researchers and dentists understand the risk factors associated with childhood, adolescent, and early adulthood caries.
Steve Levy, Wright-Bush-Shreves Endowed Professor of Research at the University of Iowa College of Dentistry, and Chandler Pendleton, biostatistician in the Iowa Institute for Oral Health Research, received a new grant to analyze data obtained from the Iowa Fluoride Study using a new statistical analysis tool that can improve understanding and identify relationships between caries and fluorosis.
The grant is a subcontract is from an NIH R03 awarded to Somnath Datta, professor of biostatics at University of Florida to collaborate on developing and implementing new specialized statistical analyses of Iowa Fluoride Study (IFS) data.
These rich and complex data allow development of models to study two important oral health conditions, dental caries and dental 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, and development of a joint model for caries and fluorosis 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 five, nine, thirteen, seventeen, and twenty-three (Aim 1a). Alongside, we will develop a new longitudinal ordinal data regression model and use it to analyze the fluorosis data at ages nine, thirteen, seventeen, and twenty-three (Aim 1b).
Finally, we will develop a joint longitudinal model when one component is count and the other ordinal, and to use it for the caries and fluorosis data together to obtain more precise estimates and to determine the time-varying correlations between the caries and fluorosis experience (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 principal investigator's website and through the Comprehensive R Archive Network.
With new insights gleaned from the new statistical method, the findings will improve our understanding of the relationships between dental caries, fluorosis, calcium-levels, sugary-beverage intake, and so forth.