Propensity score model 구축에서 상관성을 고려한 변수선택
Variable Selection for Propensity Score Models Considering the Correlations between Covariates
2
조회
0
다운로드
4
인용
0
참고문헌
키워드
초록
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 collinearityand 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 decreasedas 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 correlationof covariates, treatment variable, and outcome variable than using all the covariates observed.
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 collinearityand 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 decreasedas 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 correlationof covariates, treatment variable, and outcome variable than using all the covariates observed.
참고문헌 (15)
- Sung YN, "Reassessment of risk factors for the development of liver cirrhosis based on propensity score matching", pp.2004
- Alan A, "Effects and non-effects of paired identical observations in comparing proportion with binary matched-pairs data", Statistics in Medicine, 23(1), pp.65~75 (2004)
- Alan B, "Variable selection for propensity score models", American Journal of Epidemiology, 163(12), pp.1149~1156 (2006)
- Austin PC, "A comparison of the ability of different propensity score models to balance measured variables between treated and untreated subjects: a Monte Carlo study", Statistics in Medicine, 26(4), pp.734~753 (2007)
- Rosenbaum PR, "The central role of the propensity score in observational studies for causal effects", Biometrika, 70(1), pp.41~55 (1983)
- Marcelo CP, "Local and Global Optimal Propensity Score Matching", SAS Global Forum 2007, pp.184~185 (2007)
- Rosenbaum PR, "Optimal matching for observational studies", Journal of the American Statistical Association, 84(408), pp.1024~1032 (1989)
- Ralph BD'Agostino, "Propensity score methods for bias reduction in the comparison of a treatment to a non-randomized control group", Statistics in Medicine, 17(19), pp.2265~2281 (1998)
- Normand ST, "Validating recommendations for coronary angiography following acute myocardial infarction in the elderly", Journal of Clinical Epidemiology, 54(4), pp.387~398 (2001)
- Ming K, "A note on optimal matching with variable controls using the assignment algorithm", Journal of the American Statistical Association, 10, pp.455~463 (2001)
- Maldonado G, "Estimating causal effects", International Journal of Epidemiology, 31(2), pp.422~429 (2002)
- Binder K, "Monte Carlo Simulation in Statistical Physics", pp.128~224 (1997), Springer
- Brokhart MA, "Variable selection for propensity score models", American Journal of Epidemiology, 163(12), pp.1149~1156 (2006)
- Jones O, "Introduction to Scientific Programming and Simulation Using R", pp.150~246 (2009), Chapman & Hall CRC
- Weitzen S, "Principles for modeling propensity scores in medical research: a systematic literature review", Pharmacoepidemiology & Drug Safety, 13(12), pp.841~853 (2004)