• 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 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.

• 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.

• 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.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.2
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• pp.257-265
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• 2023

This paper investigates the determinants of credit accessibility and the effect of credit on the income of farm households borrowing from Vietnam Bank for Agriculture and Rural Development, Giong Rieng District Branch, Kien Giang Province. Based on the primary data of 200 farming households who are the customer of the bank, the study applied the Probit regression model to examine determinant factors of credit accessibility of farm households and employed the Propensity score matching method to investigate the impact of credit on households' income. The findings of the Probit regression shown that three independent variables that significantly influence the access to credit of households are household size, income source, and farm size. Besides that, the Propensity score matching method results showed a difference of 23.799 million VND/year between the income of borrowing households and that of non-borrowing households at the significance level of 1%. The difference in the imcome from the interval and central matching methods are VND 24.700 million VND/year and VND 24.633 million VND/year, respectively. Given empirical findings suggetsted that several recommendations to increase the credit accessibility of farm households, thereby creating favorable conditions for improving their income.

• 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.

• 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.

• The Journal of Fisheries Business Administration
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• v.53 no.3
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• pp.85-101
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• 2022

This study analyzed the performance of the national fishing port development project, which lacked ex-post impact evaluation despite a lot of investment in terms of fishery income opportunities. Using micro data from the Census of Agriculture, Forestry, and Fisheries, the sales amount of fishery products and the proportion of fishery-related businesses were used as performance indicators. The fishery households in the fishing port area (treatment group) and those not in the area (control group) were classified through data pre-processing, and factors unrelated to the fishing ports were controlled using the propensity score matching difference-in-difference method. The analysis target is six fishing ports with large investment in from 2010 to 2014. As a result of the analysis, it was confirmed that the sales of fishery products increased significantly in four of the six fishing ports, and the proportion of fishery-related businesses increased in two fishing ports. The analysis method of this study can be fully utilized in the evaluation of the Fishing Community New Deal 300 Project, which is in need of performance analysis.

• Genomics & Informatics
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• v.17 no.4
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• pp.47.1-47.12
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• 2019

The achievements of genome-wide association studies have suggested ways to predict diseases, such as type 2 diabetes (T2D), using single-nucleotide polymorphisms (SNPs). Most T2D risk prediction models have used SNPs in combination with demographic variables. However, it is difficult to evaluate the pure additive contribution of genetic variants to classically used demographic models. Since prediction models include some heritable traits, such as body mass index, the contribution of SNPs using unmatched case-control samples may be underestimated. In this article, we propose a method that uses propensity score matching to avoid underestimation by matching case and control samples, thereby determining the pure additive contribution of SNPs. To illustrate the proposed propensity score matching method, we used SNP data from the Korea Association Resources project and reported SNPs from the genome-wide association study catalog. We selected various SNP sets via stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and the elastic-net (EN) algorithm. Using these SNP sets, we made predictions using SLR, LASSO, and EN as logistic regression modeling techniques. The accuracy of the predictions was compared in terms of area under the receiver operating characteristic curve (AUC). The contribution of SNPs to T2D was evaluated by the difference in the AUC between models using only demographic variables and models that included the SNPs. The largest difference among our models showed that the AUC of the model using genetic variants with demographic variables could be 0.107 higher than that of the corresponding model using only demographic variables.