title: Predictive power of various models for longitudinal attachment level change.
abstract: Several statistical models that have been suggested in the periodontal literature for describing longitudinal attachment level changes, such as the gradual loss, single-burst, multiple-burst, and random walk models as well as other models introduced in this paper are compared by their power to predict future attachment loss. The data used in this analysis is from 1061 sites of 8 subjects, with moderate to severe periodontal disease, monitored monthly for about a year. This study found that none of the suggested models could significantly outperform the naïve mean predictor, which predicts the future attachment level from the past mean. It was also found that no single model, such as the burst, gradual, or random walk, together with measurement error can fully explain the variation in the data. These results indicate that in the course of one year, the attachment level change may not follow the same model. Consequently, a model that fits well to past data cannot be accurately extended to the future.
authors.affiliation: Department of Statistics, University of Florida, Gainesville.
authors.forename: M C
authors.initials: MC
authors.lastname: Yang
authors.forename: R G
authors.initials: RG
authors.lastname: Marks
authors.forename: W B
authors.initials: WB
authors.lastname: Clark
authors.forename: I
authors.initials: I
authors.lastname: Magnusson
mesh_terms: D004198:Disease Susceptibility; D004846:Epithelial Attachment / Q000473:pathology / Q000503:physiopathology; D006801:Humans; D015233:Models, Statistical; D010510:Periodontal Diseases / Q000473:pathology / Q000503:physiopathology*; D010514:Periodontal Pocket / Q000473:pathology / Q000503:physiopathology*; D011336:Probability; D012680:Sensitivity and Specificity; D013269:Stochastic Processes; D013997:Time Factors