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 change s of AL in a general periodontal research environment; that is, a sequ ential decision based on multiple sites. Several existing methods sugg ested in the periodontal research literature such as the tolerance, ru nning median, cusum, and regression methods as well as change-point de tection methods in the statistical literature are examined. It is foun d that the regression method is most convenient among the several meth ods that are equally effective in change detection. Formulae, tables a nd their usage are discussed in detail.