A Study of Effects of MultiCollinearity in the Multivariable Analysis.

International journal of applied science and technology Pub Date : 2014-10-01

Wonsuk Yoo, Robert Mayberry, Sejong Bae, Karan Singh, Qinghua Peter He, James W Lillard

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Abstract

A multivariable analysis is the most popular approach when investigating associations between risk factors and disease. However, efficiency of multivariable analysis highly depends on correlation structure among predictive variables. When the covariates in the model are not independent one another, collinearity/multicollinearity problems arise in the analysis, which leads to biased estimation. This work aims to perform a simulation study with various scenarios of different collinearity structures to investigate the effects of collinearity under various correlation structures amongst predictive and explanatory variables and to compare these results with existing guidelines to decide harmful collinearity. Three correlation scenarios among predictor variables are considered: (1) bivariate collinear structure as the most simple collinearity case, (2) multivariate collinear structure where an explanatory variable is correlated with two other covariates, (3) a more realistic scenario when an independent variable can be expressed by various functions including the other variables.

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多重共线性在多变量分析中的作用研究。

在调查危险因素和疾病之间的关系时，多变量分析是最流行的方法。然而，多变量分析的效率很大程度上取决于预测变量之间的相关结构。当模型中的协变量不是相互独立时，分析中会出现共线性/多重共线性问题，从而导致有偏估计。本研究的目的是在不同共线性结构的情况下进行模拟研究，以研究预测变量和解释变量之间不同相关结构下共线性的影响，并将这些结果与现有的确定有害共线性的指南进行比较。我们考虑了预测变量之间的三种相关情况:(1)最简单的共线性情况是二元共线性结构;(2)一个解释变量与另外两个协变量相关的多元共线性结构;(3)一个自变量可以用包括其他变量在内的各种函数来表示的更现实的情况。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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International journal of applied science and technology

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