Bender Ralf
Institute for Quality and Efficiency in Health Care, Cologne, Germany.
Methods Mol Biol. 2009;471:179-95. doi: 10.1007/978-1-59745-416-2_9.
Regression modeling is one of the most important statistical techniques used in analytical epidemiology. By means of regression models the effect of one or several explanatory variables (e.g., exposures, subject characteristics, risk factors) on a response variable such as mortality or cancer can be investigated. From multiple regression models, adjusted effect estimates can be obtained that take the effect of potential confounders into account. Regression methods can be applied in all epidemiologic study designs so that they represent a universal tool for data analysis in epidemiology. Different kinds of regression models have been developed in dependence on the measurement scale of the response variable and the study design. The most important methods are linear regression for continuous outcomes, logistic regression for binary outcomes, Cox regression for time-to-event data, and Poisson regression for frequencies and rates. This chapter provides a nontechnical introduction to these regression models with illustrating examples from cancer research.
回归建模是分析性流行病学中最重要的统计技术之一。通过回归模型，可以研究一个或多个解释变量（如暴露因素、个体特征、风险因素）对诸如死亡率或癌症等反应变量的影响。从多元回归模型中，可以获得考虑了潜在混杂因素影响的调整效应估计值。回归方法可应用于所有流行病学研究设计，因此它们是流行病学数据分析的通用工具。根据反应变量的测量尺度和研究设计，已开发出不同类型的回归模型。最重要的方法有：用于连续结局的线性回归、用于二元结局的逻辑回归、用于事件发生时间数据的Cox回归以及用于频率和率的泊松回归。本章通过癌症研究的示例对这些回归模型进行非技术性介绍。
