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统计入门:多变量回归的注意事项和陷阱。

Statistical primer: multivariable regression considerations and pitfalls.

机构信息

Academic Surgery Unit, Institute of Cardiovascular Sciences, University of Manchester, ERC, Wythenshawe Hospital, Manchester, UK.

Coronary and Structural Heart, Medtronic, Watford, Herts, UK.

出版信息

Eur J Cardiothorac Surg. 2019 Feb 1;55(2):179-185. doi: 10.1093/ejcts/ezy403.


DOI:10.1093/ejcts/ezy403
PMID:30596979
Abstract

Multivariable regression models are used to establish the relationship between a dependent variable (i.e. an outcome of interest) and more than 1 independent variable. Multivariable regression can be used for a variety of different purposes in research studies. The 3 most common types of multivariable regression are linear regression, logistic regression and Cox proportional hazards regression. A detailed understanding of multivariable regression is essential for correct interpretation of studies that utilize these statistical tools. This statistical primer discusses some common considerations and pitfalls for researchers to be aware of when undertaking multivariable regression.

摘要

多变量回归模型用于建立一个因变量(即感兴趣的结果)与多个自变量之间的关系。多变量回归可用于研究中的各种不同目的。多变量回归最常见的三种类型是线性回归、逻辑回归和 Cox 比例风险回归。正确解释使用这些统计工具的研究,必须对多变量回归有详细的了解。本统计入门讨论了研究人员在进行多变量回归时需要注意的一些常见考虑因素和陷阱。

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