• Journal of Applied Reliability
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• v.16 no.4
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• pp.323-330
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• 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
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• v.58 no.3
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• pp.152-168
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• 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
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• v.18 no.1
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• pp.149-167
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• 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 Korean Traditional Oncology
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• v.27 no.1
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• pp.25-36
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• 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.

• Korean Journal of Social Welfare Studies
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• no.37
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• pp.171-197
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• 2008

This study focuses on the following question; self-supporting training program increases participants' income compare to non-participants who have similar characteristics. This question is based on counterfactual assumption. In other words, this study concentrates on what the outcomes would have been if the participants were to be absent. This study adopts a quasi-experimental design. To overcome previous study's methodological weaknesses, especially selection bias, I applied matching procedure based on a propensity-score matching. Matching process was performed by using 'MatchIt' software. The major findings are as follows From Least Squares Regression analysis, I found the poor's income are significantly different according to age, pre-intervention earning, material status, and participation of training. Since the poor have homogeneous education level, education variable was not statistically significant. From the Simulation Quantities of Interest analysis, I also found that treatment group's expected incomes are lower than control's expected incomes. In other words, participation of training has a negative effect on the participants' earnings.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.1-19
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• 2023

Observational data with missing or incomplete data are common in biomedical research. Multiple imputation is an effective approach to handle missing data with the ability to decrease bias while increasing statistical power and efficiency. In recent years propensity score (PS) matching has been increasingly used in observational studies to estimate treatment effect as it can reduce confounding due to measured baseline covariates. In this paper, we describe in detail approaches to competing risk analysis in the setting of incomplete observational data when using PS matching. First, we used multiple imputation to impute several missing variables simultaneously, then conducted propensity-score matching to match statin-exposed patients with those unexposed. Afterwards, we assessed the effect of statin exposure on the risk of heart failure-related hospitalizations or emergency visits by estimating both relative and absolute effects. Collectively, we provided a general methodological framework to assess treatment effect in incomplete observational data. In addition, we presented a practical approach to produce overall cumulative incidence function (CIF) based on estimates from multiple imputed and PS-matched samples.

• Survey Research
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• v.10 no.1
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• pp.169-186
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• 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 Sasang Constitutional Medicine
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• v.28 no.1
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• pp.11-18
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• 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.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.91-100
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• 2016

Fuzzy matching is proposed to make propensities of two groups similar with their propensity scores and a way to select control variable to make propensity scores with a process that shows how to acquire propensity scores using logic regression analysis, is presented. With such scores, it was a method to obtain an experiment group and a control group that had similar propensity employing the Fuzzy Matching. In the study, it was proven that the two groups were the same but with a different distribution chart and standardization which made edge tolerance different and we realized that the number of chosen cases decreased when the edge tolerance score became smaller. So with the idea, we were able to determine that it is possible to merge groups using fuzzy matching without a precontrol and use them when data (big data) are used while to check the pros and cons of Fuzzy Matching were made possible.

• Journal of Korean Academy of Nursing
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• v.37 no.3
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• pp.414-421
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• 2007

Background: Nursing intervention studies often suffer from a selection bias introduced by failure of random assignment. Evaluation with selection bias could under or over-estimate any intervention's effects. PS matching (PSM) can reduce a selection bias through matching similar Propensity Scores (PS). PS is defined as the conditional probability of being treated given the individual's covariates and it can be reused to balance the covariates of two groups. Purpose: This study was done to assess the significance of PSM as an alternative evaluation method of nursing interventions. Method: An intervention study for patients with some baseline individual characteristic differences between two groups was used for this demonstration. The result of a t-test with PSM was compared with a t-test without matching. Results: The level of HbA1c at 12 months after baseline was different between the two groups in terms of matching or not. Conclusion: This study demonstrated the effects of a quasi-random assignment. Evaluation using PSM can reduce a selection bias impact that affects the result of the nursing intervention. Analyzing nursing research more objectively to reduce selection bias using PSM is needed.