Wang Q Q, Yu S C, Qi X, Hu Y H, Zheng W J, Shi J X, Yao H Y
Office of Epidemiology, Chinese Center for Disease Control and Prevention, Beijing 102206, China.
Zhonghua Yu Fang Yi Xue Za Zhi. 2019 Sep 6;53(9):955-960. doi: 10.3760/cma.j.issn.0253-9624.2019.09.018.
Logistic regression is a kind of multiple regression method to analyze the relationship between a binary outcome or categorical outcome and multiple influencing factors, including multiple logistic regression, conditional logistic regression, polytomous logistic regression, ordinal logistic regression and adjacent categorical logistic regression. This paper illustrates the basic principle, independent variable selection and assignment, applied condition, model evaluation and diagnosis for multiple logistic regression model. Moreover, the principle and application for polytomous logistic regression and ordinal logistic regression models were also introduced. By providing SAS codes and detailed explanations of the result for an example of obesity, readers could be able to better understand logistic regression model, and apply this method correctly to their research and daily work, so as to improve their capacity of the data analysis.
逻辑回归是一种多元回归方法，用于分析二元结局或分类结局与多个影响因素之间的关系，包括多元逻辑回归、条件逻辑回归、多分类逻辑回归、有序逻辑回归和相邻分类逻辑回归。本文阐述了多元逻辑回归模型的基本原理、自变量的选择与赋值、应用条件、模型评估与诊断。此外，还介绍了多分类逻辑回归和有序逻辑回归模型的原理及应用。通过给出一个肥胖示例的SAS代码及结果详细解释，读者能够更好地理解逻辑回归模型，并将该方法正确应用于自己的研究和日常工作中，从而提高其数据分析能力。
