Clin Should Elbow.  2013 Jun;16(1):63-72.

Multivariate Analysis for Clinicians

• Oh JH
• Chung SW

Abstract

In medical research, multivariate analysis, especially multiple regression analysis, is used to analyze the influence of multiple variables on the result. Multiple regression analysis should include variables in the model and the problem of multi-collinearity as there are many variables as well as the basic assumption of regression analysis. The multiple regression model is expressed as the coefficient of determination, R2 and the influence of independent variables on result as a regression coefficient, beta. Multiple regression analysis can be divided into multiple linear regression analysis, multiple logistic regression analysis, and Cox regression analysis according to the type of dependent variables (continuous variable, categorical variable (binary logit), and state variable, respectively), and the influence of variables on the result is evaluated by regression coefficient beta, odds ratio, and hazard ratio, respectively. The knowledge of multivariate analysis enables clinicians to analyze the result accurately and to design the further research efficiently.

MeSH Terms

Linear Models
Logistic Models
Multivariate Analysis
Odds Ratio