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Clin Shoulder Elbow > Volume 16(1); 2013 > Article
Clinics in Shoulder and Elbow 2013;16(1):63-72.
DOI: https://doi.org/10.5397/CiSE.2013.16.1.63    Published online June 30, 2013.
Multivariate Analysis for Clinicians
Joo Han Oh, Seok Won Chung
1Department of Orthopaedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Korea.
2Department of Orthopaedic Surgery, Konkuk University School of Medicine, Konkuk University Medical Center, Korea. smilecsw@gmail.com
임상의를 위한 다변량 분석의 실제
오주한·정석원*
서울대학교 의과대학 정형외과학교실, 분당서울대학교병원 관절센터, 건국대학교 의학전문대학원 정형외과학교실*
건국대학교병원 어깨팔꿈치관절센터
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.
Key Words: Multivariate analysis; Multiple regression analysis; Multiple linear regression analysis; Multiple logistic regression analysis; Cox regression analysis
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