流行病学研究中回归分析的多重共线性
Multicollinearity in Regression Analyses Conducted in Epidemiologic Studies.
作者信息
Vatcheva Kristina P, Lee MinJae, McCormick Joseph B, Rahbar Mohammad H
机构信息
Division of Epidemiology, University of Texas Health Science Center-Houston, School of Public Health, Brownsville Campus, Brownsville, TX.
Division of Clinical and Translational Sciences, Department of Internal Medicine, University of Texas Medical School, Biostatistics/Epidemiology/Research Design (BERD) Core, Center for Clinical and Translational Sciences (CCTS), The University of Texas Health Science Center at Houston, Houston, TX.
出版信息
Epidemiology (Sunnyvale). 2016 Apr;6(2). doi: 10.4172/2161-1165.1000227. Epub 2016 Mar 7.
The adverse impact of ignoring multicollinearity on findings and data interpretation in regression analysis is very well documented in the statistical literature. The failure to identify and report multicollinearity could result in misleading interpretations of the results. A review of epidemiological literature in PubMed from January 2004 to December 2013, illustrated the need for a greater attention to identifying and minimizing the effect of multicollinearity in analysis of data from epidemiologic studies. We used simulated datasets and real life data from the Cameron County Hispanic Cohort to demonstrate the adverse effects of multicollinearity in the regression analysis and encourage researchers to consider the diagnostic for multicollinearity as one of the steps in regression analysis.
忽视多重共线性对回归分析中研究结果及数据解释的不利影响，在统计文献中有充分记载。未能识别和报告多重共线性可能导致对结果的误导性解释。对2004年1月至2013年12月PubMed上的流行病学文献进行回顾，表明在分析流行病学研究数据时，需要更加关注识别和最小化多重共线性的影响。我们使用模拟数据集和卡梅伦县西班牙裔队列的实际生活数据，来证明多重共线性在回归分析中的不利影响，并鼓励研究人员将多重共线性诊断视为回归分析的步骤之一。
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