Change detection on longitudinal data in periodontal research.

Longitudinal data of attachment level (AL) or the alveolar bone level are often used to assess the progression of periodontal disease. This paper tries to identify the most efficient method to detect the changes of AL in a general periodontal research environment; that is, a sequential decision based on multiple sites. Several existing methods suggested in the periodontal research literature such as the tolerance, running median, cusum, and regression methods as well as change-point detection methods in the statistical literature are examined. It is found that the regression method is most convenient among the several methods that are equally effective in change detection. Formulae, tables and their usage are discussed in detail.