Ante-dependence modeling in a longitudinal study of periodontal disease: The effect of age, gender, and smoking status
Ante-dependence modeling in a longitudinal study of periodontal disease: The effect of age, gender, and smoking status
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DOI：
10.1902/jop.2000.71.3.454
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发表时间：
2000-03-01
影响因子：
4.3
通讯作者：
Seymour, GJ
中科院分区：
文献类型：
--
作者：
Faddy, MJ;Cullinan, MP;Seymour, GJ
背景：人们普遍认为，牙周病的进展是由一系列的突发性疾病引起的，其间穿插着稳定期，甚至是附着能力的增强。为了分析患者疾病经历的纵向数据，有必要使用适应序列依赖的模型。随着时间推移，一系列牙周检查结果之间的前相关性可以用马尔科夫链来建模。这个模型用转移概率描述了患者疾病水平的时间变化，这允许疾病的退化和进展。本研究的目的是演示如何使用马尔可夫链模型来分析来自一项调查成人人群牙周病进展的纵向研究的数据。方法：研究人群由504名志愿者组成；然而，由于其余48名受试者没有给出连续的数据，因此只有456名志愿者被纳入分析。受试者在基线、6个月、1、2和3年进行检查。使用自动探头记录探测深度(PD)。疾病定义为PD大于或等于4 mm的四个或更多部位。采用马尔可夫链模型分析年龄、性别和吸烟对牙周病自然进展和消退(愈合)的影响。结果：吸烟和年龄增长对牙周病的自然进展和消退(愈合)没有影响，但在减少疾病消退方面有显著作用(P值小于或等于0.05)，即它们对疾病的影响似乎是对自然愈合过程的抑制。性别没有显著的影响。结论：这些结果表明，纵向数据的事前依赖建模可以揭示从数据中可能不立即明显的影响，吸烟和年龄增加被认为抑制了康复过程，而不是促进了疾病的进展。
Background: It is generally accepted that periodontal disease progresses by a series of bursts that are interspersed by periods of stability or even gain of attachment. In order to analyze longitudinal data on a patient's disease experience, it is necessary to use models which accommodate serial dependence. Ante-dependence between the results of a series of periodontal examinations over time can be modeled using a Markov chain. This model describes temporal changes in patients' levels of disease in terms of transition probabilities, which allow for both regression and progression of the disease. The aim of the present study was to demonstrate the use of a Markov chain model to analyze data from a longitudinal study investigating the progression of periodontal disease in an adult population.Methods: The study population consisted of 504 volunteers; however, only 456 were included in the analysis because the remaining 48 subjects did not give consecutive data. Subjects were examined at baseline, 6 months, and 1, 2, and 3 years. Probing depths (PD) were recorded using an automated probe. Disease was defined as four or more sites with PD greater than or equal to 4 mm. Markov chain modeling was used to determine the effect of age, gender, and smoking on the natural progression and regression (healing) of periodontal disease.Results: Smoking and increasing age had no effect on the progression of disease in this population, but did have a significant effect (P values less than or equal to 0.05) in reducing the regression of disease; i.e., their effect on disease appears to be inhibition of the natural healing process. Gender had no significant effects.Conclusions: These results demonstrate how ante-dependence modeling of longitudinal data can reveal effects that may not be immediately apparent from the data, with smoking and increasing age being seen to inhibit the healing process rather than promote disease progression.