|
KMID : 1155220150400010075
|
|
Journal of the Korean Society of Health Information and Health Statistics
2015 Volume.40 No. 1 p.75 ~ p.86
|
|
|
Variable Selection for Propensity Score Models Considering the Correlations between Covariates
|
|
Park Seong-Hun
Song Ki-Jun
|
|
|
Abstract
|
|
|
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.
|
|
|
KEYWORD
|
|
|
Propensity score, Matching, Simulation, Variable selection
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
|