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Other Titles
 Bootstrap estimation of the standard error of treatment effect with double propensity score adjustment 
 임소정  ;  정인경 
 Korean Journal of Applied Statistics (응용통계연구), Vol.30(3) : 453-462, 2017 
Journal Title
 Korean Journal of Applied Statistics (응용통계연구) 
Issue Date
Double propensity score adjustment is an analytic solution to address bias due to incomplete matching. However, it is difficult to estimate the standard error of the estimated treatment effect when using double propensity score adjustment. In this study, we propose two bootstrap methods to estimate the standard error. The first is a simple bootstrap method that involves drawing bootstrap samples from the matched sample using the propensity score as well as estimating the standard error from the bootstrapped samples. The second is a complex bootstrap method that draws bootstrap samples first from the original sample and then applies the propensity score matching to each bootstrapped sample. We examined the performances of the two methods using simulations under various scenarios. The estimates of standard error using the complex bootstrap were closer to the empirical standard error than those using the simple bootstrap. The simple bootstrap methods tended to underestimate. In addition, the coverage rates of a 95% confidence interval using the complex bootstrap were closer to the advertised rate of 0.95. We applied the two methods to a real data example and found also that the estimate of the standard error using the simple bootstrap was smaller than that using the complex bootstrap.
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1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
Yonsei Authors
Jung, Inkyung(정인경) ORCID logo https://orcid.org/0000-0003-3780-3213
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