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解读龋齿研究中的泊松回归模型。

Interpreting Poisson Regression Models in Dental Caries Studies.

出版信息

Caries Res. 2018;52(4):339-345. doi: 10.1159/000486970. Epub 2018 Feb 23.

Abstract

Oral epidemiology involves studying and investigating the distribution and determinants of dental-related diseases in a specified population group to inform decisions in the management of health problems. In oral epidemiology studies, the hypothesis is typically followed by a cogent study design and data collection. Appropriate statistical analysis is essential to demonstrate the scientific association between the independent factors and the target variable. Analysis also helps to develop and build a statistical model. Poisson regression and its extensions have gained more attention in caries epidemiology than other working models such as logistic regression. This review discusses the fundamental principles and basic knowledge of Poisson regression models. It also introduces the use of a robust variance estimator with a focus on the "robust" interpretation of the model. In addition, extensions of regression models, including the zero-inflated model, hurdle model, and negative binomial model, and their interpretation in caries studies are reviewed. Principles of model fitting, including goodness-of-fit measures, are also discussed. Clinicians and researchers should pay attention to the statistical context of the models used and interpret the models to improve the oral and general health of the communities in which they live.

摘要

口腔流行病学涉及研究和调查特定人群中与牙齿相关的疾病的分布和决定因素,以便为健康问题的管理决策提供信息。在口腔流行病学研究中,通常先提出假设,然后再进行合理的研究设计和数据收集。适当的统计分析对于证明独立因素与目标变量之间的科学关联至关重要。分析还有助于开发和构建统计模型。与其他工作模型(如逻辑回归)相比,泊松回归及其扩展在龋齿流行病学中得到了更多关注。本综述讨论了泊松回归模型的基本原理和基础知识。它还介绍了使用稳健方差估计量的方法,重点是对模型的“稳健”解释。此外,还回顾了回归模型的扩展,包括零膨胀模型、障碍模型和负二项模型,以及它们在龋齿研究中的解释。还讨论了模型拟合的原则,包括拟合优度度量。临床医生和研究人员应注意所使用模型的统计背景,并对模型进行解释,以改善他们所在社区的口腔和整体健康。

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