• Journal of the Korean Data and Information Science Society
• /
• v.26 no.1
• /
• pp.217-227
• /
• 2015

In observational study, handling confounders is a primary issue in measuring treatment effect of interest. Historically, a regression with covariate adjustment (covariate-adjusted regression) has been the typical approach to estimate treatment effect incorporating potential confounders into model. However, ever since the introduction of the propensity score, covariate-adjusted regression has been gradually replaced in medical literatures with various balancing methods based on propensity score. On the other hand, there is only a paucity of researches assessing propensity score methods compared with the covariate-adjusted regression. This paper examined the performance of propensity score methods in estimating risk difference and compare their performance with the covariate-adjusted regression by a Monte Carlo study. The study demonstrated in general the covariate-adjusted regression with variable selection procedure outperformed propensity-score-based methods in terms both of bias and MSE, suggesting that the classical regression method needs to be considered, rather than the propensity score methods, if a performance is a primary concern.

• Survey Research
• /
• v.10 no.1
• /
• pp.169-186
• /
• 2009

The propensity score method is used to minimize the bias level in social survey, which comes from nonresponse. The theoretical concept and the background of the propensity score method is discussed first. The propensity score method was first applied in the epidemiology observational study. I have summarized the process of the three propensity score methods that were used to reduce estimation bias in this study. Matching by propensity score is applied to the relatively large control group. Subclassification has the advantage of using whole control group data and regression adjustment is applied to multiple covariates as well as propensity score of each unit is computable and usable. Lastly, the application procedures of propensity score method to reduce the nonresponse bias is suggested and its applicability to real situation is reviewed with the existing data.

• Journal of Applied Reliability
• /
• v.16 no.4
• /
• pp.323-330
• /
• 2016

Purpose: Recently, propensity score matching method is used in a large number of research paper, nonetheless, there is no research using fitness test of before and after propensity score matching. Therefore, comparing fitness of before and after propensity score matching by logistic regression analysis using data from 'online survey of adolescent health' is the main significance of this research. Method: Data that has similar propensity in two groups is extracted by using propensity score matching then implement logistic regression analysis on before and after matching separately. Results: To test fitness of logistic regression analysis model, we use Model summary, -2Log Likelihood and Hosmer-Lomeshow methods. As a result, it is confirmed that the data after matching is more suitable for logistic regression analysis than data before matching. Conclusion: Therefore, better result which has appropriate fitness will be shown by using propensity score matching shows better result which has better fitness.

• The Journal of the Korean dental association
• /
• v.58 no.3
• /
• pp.152-168
• /
• 2020

The propensity score matching method is a statistical method used to reduce selection bias in observational studies and to show effects similar to random allocation. There are many observational studies in dentistry research, and differences in baseline covariates between the control and case groups affect the outcome. In order to reduce the bias due to confounding variables, the propensity scores are used by equating groups based on the baseline covariates. This method is effective, especially when there are many covariates or the sample size is small. In this paper, the propensity score matching method was explained in a simple way with a dental example by using R software. This simulated data were obtained from one of retrospective study. The control group and the case group were matched according to the propensity score and compared before and after treatment. The propensity score matching method could be an alternative to compensate for the disadvantage of the observation study by reducing the bias based on the covariates with the propensity score.

• Journal of the Korean School Mathematics Society
• /
• v.18 no.1
• /
• pp.149-167
• /
• 2015

In this study, we estimate the effect of ability grouping on mathematics achievement empirically. We use propensity score matching(PSM) method to minimize selection bias and estimate the effect of ability grouping on the mathematics standard score of Scholastic Ability Test with the KELS(Korea Education Longitudinal Study) 6th stage data. The result indicated that relationship between ability grouping and mathematics achievement is positive and Policy efforts is needed to operate ability grouping effectively.

• Journal of Preventive Medicine and Public Health
• /
• v.40 no.6
• /
• pp.495-504
• /
• 2007

Objectives : This study attempts to show how studies using non-experimental data can strengthen causal inferences by applying propensity score and instrumental variable methods based on the counterfactual framework. For illustrative purposes, we examine the effect of having private health insurance on the probability of experiencing at least one hospital admission in the previous year. Methods : Using data from the 4th wave of the Korea Labor and Income Panel Study, we compared the results obtained using propensity score and instrumental variable methods with those from conventional logistic and linear regression models, respectively. Results : While conventional multiple regression analyses fail to identify the effect, the results estimated using propensity score and instrumental variable methods suggest that having private health insurance has positive and statistically significant effects on hospital admission. Conclusions : This study demonstrates that propensity score and instrumental variable methods provide potentially useful alternatives to conventional regression approaches in making causal inferences using non-experimental data.

• Journal of Korean Traditional Oncology
• /
• v.27 no.1
• /
• pp.25-36
• /
• 2022

The Randomized Control Trial (RCT) is the most well-established and widely used statistical methodology in clinical research; however, applying thorough RCT to cancer patients presents challenges such as ethical concerns, high costs, short clinical periods, and limitations in collecting various side effects. To address this issue, the propensity score matching method, which takes advantage of the benefits of observational research while compensating for the drawbacks of randomized control trials, is used in a variety of fields. In recent years, 28 studies on the effectiveness of Korean medicine on tumors have been conducted abroad using the Propensity Score Matching Method, but none have been conducted in Korea. The majority of studies have focused on liver cancer, colon cancer, lung cancer, and stomach cancer, with endpoints such as survival time, incidence rate, quality of life, and treatment outcomes revealing statistical differences in how Korean medicine intervention affects treatment outcomes. As a result, well-established studies using the propensity matching score methodology should be useful in evaluating the impact of Korean medicine in oncology treatments.

• Survey Research
• /
• v.10 no.3
• /
• pp.21-36
• /
• 2009

This study suggests the applicability of web surveys regarding elections in order to contact a great number of young people. The propensity weighting model was estimated using the demographic variables and the covariate variables collected during the 2007 presidential election surveys. In order to adjust the internet survey to the telephone survey, we used the propensity score method. Propensity score weighting made the internet survey results closer to the telephone survey results. This shows that an internet survey with propensity weighting model is a potential alternative survey method in the prediction of elections.

• The Korean Journal of Applied Statistics
• /
• v.30 no.3
• /
• pp.453-462
• /
• 2017

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.

• Journal of Sasang Constitutional Medicine
• /
• v.28 no.1
• /
• pp.11-18
• /
• 2016

Objectives The body shape was a key information for diagnosing the Sasang constitution (SC) in Sasang constitutional medicine. The aim of this study was to compare the body shapes and mainly focusing on the Korean and Chinese-KoreanMethods We calculated the propensity score for each SC type in males and females separately, and compared body shape including 8 circumference and 5 width between Korean and Chinese-Korean according to the sex and SC.Results Koreans have larger trunk and hip area compared to Chinese-Koreans, while Chinese-Koreans have larger abdomen compared with Koreans. Most variables were significantly different among SC types, for both Korean and Chinese-Korean. Especially, the Taeumin (TE) type has the largest body shape compared with the other SC types, it was similar between Korean and Chinese-Korean.Conclusions This study showed that the TE type has the largest body shape, followed by Soyangin (SY) and Soeumin (SE) in order, for both Korean and Chinese-Korean respectively. These results suggests that the body shape of Chinese-Korean is similar with Korean based on SC type.