470 355

Cited 0 times in

Propensity score model 구축에서 상관성을 고려한 변수선택

Other Titles
 Variable Selection for Propensity Score Models Considering the Correlations between Covariates 
 박성훈  ;  송기준 
 Journal of Health Informatics and Statistics (보건정보통계학회지), Vol.40(1) : 75-86, 2015 
Journal Title
Journal of Health Informatics and Statistics(보건정보통계학회지)
Issue Date
Propensity score ; Matching ; Simulation ; Variable selection
Objectives: In the covariate selection for propensity score model (PSM), including all the covariates that can be observed has been recommended. However, there are problems that appear multi collinearity and do not obtain the matching number needed using over fitted propensity score model. In this study, we studied the method of variable selection for PSM considering the correlations between covariates.
Methods: All the covariates were classified according to the relation with treatment and outcome and generated considering the correlations each other. We examined the odds ratio and MSE (mean
squared error) of PSM and the matching number of simulated data.
Results: When there are correlations among covariates included in PSM, the matching number decreased as the correlation of covariates was stronger. Also, the larger the strength of correlation among
covariates was, the smaller MSE was and the matching number was.
Conclusions: When including covariates in PSM, we found that it is more efficient to examine the correlation of covariates, treatment variable, and outcome variable than using all the covariates observed.
Files in This Item:
T201501662.pdf Download
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
Yonsei Authors
Park, Seong Hun(박성훈)
Song, Ki Jun(송기준) ORCID logo https://orcid.org/0000-0003-2505-4112
사서에게 알리기


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.