线性回归分析：系列评估科学出版物之十四。
Linear regression analysis: part 14 of a series on evaluation of scientific publications.
机构信息
Departrment of Medical Biometrics, Epidemiology, and Computer Sciences, Johannes Gutenberg University, Mainz, Germany.
出版信息
Dtsch Arztebl Int. 2010 Nov;107(44):776-82. doi: 10.3238/arztebl.2010.0776. Epub 2010 Nov 5.
BACKGROUND
Regression analysis is an important statistical method for the analysis of medical data. It enables the identification and characterization of relationships among multiple factors. It also enables the identification of prognostically relevant risk factors and the calculation of risk scores for individual prognostication.
METHODS
This article is based on selected textbooks of statistics, a selective review of the literature, and our own experience.
RESULTS
After a brief introduction of the uni- and multivariable regression models, illustrative examples are given to explain what the important considerations are before a regression analysis is performed, and how the results should be interpreted. The reader should then be able to judge whether the method has been used correctly and interpret the results appropriately.
CONCLUSION
The performance and interpretation of linear regression analysis are subject to a variety of pitfalls, which are discussed here in detail. The reader is made aware of common errors of interpretation through practical examples. Both the opportunities for applying linear regression analysis and its limitations are presented.
背景
回归分析是一种重要的医学数据分析方法。它可以识别和描述多个因素之间的关系。它还可以识别预后相关的风险因素，并为个体预后计算风险评分。
方法
本文基于统计学的精选教材、文献的选择性回顾以及我们自己的经验。
结果
在简要介绍单变量和多变量回归模型后，给出了说明性示例，解释了在进行回归分析之前需要考虑哪些重要因素，以及如何解释结果。然后，读者应该能够判断方法是否正确使用，并适当解释结果。
结论
线性回归分析的性能和解释受到多种缺陷的影响，本文详细讨论了这些缺陷。通过实际示例，使读者注意到常见的解释错误。本文介绍了线性回归分析的应用机会及其局限性。
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