Analytic approaches to longitudinal caries data in adults

The objective of this paper is to consider current methods for analyzing longitudinal caries data in adults. To illustrate these methods, we used data from the Piedmont dental study, a prospective investigation of the oral health of older adults. Longitudinal dental data sets comprise repeated observations of an outcome (often clustered within randomly selected primary sampling units), and a set of covariates for each of many subjects, in whom clustering can occur as a result of measuring teeth, or surfaces, within people. One objective of statistical analysis is to predict the outcome variable as a function of the covariates, while accounting for the correlation among the repeated observations for a given subject and the effect of clustering within subjects, as well as between subjects within primary sampling units, such as communities, schools, hospitals, or other such units. We considered two statistical approaches: generalized estimating equations and survey regression models. We also examined the impact of varying diagnostic criteria for caries estimation between epidemiologists and clinicians. One approach is to perform the usual time(x) exam score minus time(0) score analysis for the baseline and final examinations, while an alternative is to analyze trends among interim examinations. Finally, because caries studies in which the onset of the disease is the endpoint face the problem of censoring due to subject attrition and/or tooth loss, we recommend the incidence density (time-to-event) analytic strategy to address this problem. This approach was found to be most suitable for longitudinal studies of older adults since it accounts for the time each surface remains at risk for the event of interest, making use of interim exam data until the moment the subject and/or the tooth are no longer available for examination. We also included a discussion on biases that occur upon application of the usual methods of estimating caries experience in missing teeth and crowns, which often ignore the classification error in the estimation. We propose a method to adjust for misclassification of the hi-component of the DMFS index. In the case where one can observe true reversals or remineralization of caries lesions, we recommend an adjustment formula to account for reversals that are most likely due to examiner misclassification. We provide examples to demonstrate the applicability of the methods for covariates subject to outcome misclassification.