Yang M C, Namgung Y Y, Marks R G, Magnusson I, Clark W B
Division of Biostatistics, Periodontal Disease Research Center, Gainesville, Florida.
J Periodontal Res. 1993 Mar;28(2):152-60. doi: 10.1111/j.1600-0765.1993.tb01063.x.
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.
附着水平（AL）或牙槽骨水平的纵向数据常被用于评估牙周疾病的进展。本文试图在一般的牙周研究环境中确定检测AL变化的最有效方法；即在多个部位进行序贯决策。研究了牙周研究文献中提出的几种现有方法，如容差法、移动中位数法、累积和法、回归法以及统计文献中的变点检测方法。发现在几种在变化检测中同样有效的方法中，回归法最为便捷。详细讨论了公式、表格及其用法。
