• 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.

• 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.

• 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.

• 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.

• 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.

• Health Policy and Management
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• v.25 no.1
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• pp.3-10
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• 2015

Background: Previous studies showed differences in healthcare utilization among insurance types. This study aimed to analyze the difference in healthcare utilization for percutaneous transluminal coronary angioplasty inpatients by insurance types after controlling factors affecting healthcare utilization using propensity score matching (PSM). Methods: The 2011 national inpatient sample based on health insurance claims data was used for analysis. PSM was used to control factors influencing healthcare utilization except insurance types. Length of stay and total charges were used as healthcare utilization variables. Patients were divided into National Health Insurance (NHI) and Medical Aid (MA) patients. Factors representing inpatients (gender, age, admission sources, and Elixhauser comorbidity index) and hospitals (number of doctors, number of beds, and location of hospitals) were used as covariates in PSM. Results: Tertiary hospitals didn't show significant difference in length of stay and total charges after PSM between two insurance types. However, MA patients showed significantly longer length of stay than that of NHI patients after PSM in general hospitals. Multivariate regression analysis provided that admission sources, Elixhauser comorbidity index, insurance types, number of doctors, and location of hospitals (province) had significant influences on the length of stay in general hospitals. Conclusion: Study results provided evidences that healthcare utilization was differed by insurance types in general hospitals. Health policy makers will need to prepare interventions to influence the healthcare utilization differences between insurance types.

• Health Policy and Management
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• v.26 no.4
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• pp.343-351
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• 2016

Background: This study aims to analyze the effects of insurance types on the medical service uses for heart failure inpatients using propensity score matching (PSM). Methods: 2014 National inpatient sample based on health insurance claims data was used in the analysis. PSM was applied to control factors influencing the service uses except insurance types. Negative binomial regression was used after PSM to analyze factors that had influences on the service uses among inpatients. Subjects were divided by health insurance type, national health insurance (NHI) and medical aid (MA). Total charges and length of stay were used to represent the medical service uses. Covariance variables in PSM consist of sociodemographic characteristics (gender, age, Elixhauser comorbidity index) and hospital characteristics (hospital types, number of beds, location, number of doctors per 50 beds). These variables were also used as independent variables in negative binomial regression. Results: After the PSM, length of stay showed statistically significant difference on medical uses between insurance types. Negative binomial regression provided that insurance types, Elixhauser comorbidity index, and number of doctors per 50 beds were significant on the length of stay. Conclusion: This study provided that the service uses, especially length of stay, were differed by insurance types. Health policy makers will be required to prepare interventions to narrow the gap of the service uses between NHI and MA.

• Journal of Gastric Cancer
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• v.19 no.2
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• pp.212-224
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• 2019

Purpose: Splenic hilar lymph node dissection (LND) during total gastrectomy is regarded as the standard treatment for proximal advanced gastric cancer (AGC). This study aimed to investigate whether splenic hilar LND or D2 LND is essential for proximal AGC of pT2- 4aN0M0 stage. Materials and Methods: Data of curative total gastrectomies (n=370) performed from 2000 to 2010 for proximal AGC of pT2-4aN0 stage were retrospectively reviewed. Clinicopathological characteristics and long-term outcomes were compared using propensity score matching between patients who underwent splenectomy (n=43) and those who did not (n=327) and between patients who underwent D2 LND (n=122) and those who underwent D1+ LND (n=248). Results: Tumors of larger size and a more advanced T stage and significantly lower overall and relapse-free survival (P<0.001) were observed in the splenectomy group than in the 2 spleen-preserving groups. Before propensity score matching, worse overall and relapse-free survival (P<0.001) was observed in the splenectomy group than in the non-splenectomy group. After matching, although the overall survival became similar (P=0.123), relapse-free survival was worse in the splenectomy group (P=0.021). Compared with D1+ LND, D2 LND had no positive impact on the overall (P=0.619) and relapse-free survival (P=0.112) after propensity score matching. Conclusions: Splenic hilar LND with or without splenectomy may not have an oncological benefit for patients with pathological AGC with no LN metastasis.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.415-445
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• 2023

Oftentimes, the time dependent treatment covariate and the time dependent confounders exist in observation studies. It is an important problem to correctly adjust for the time dependent confounders in the propensity score analysis. Recently, In the survival data, Hade et al. (2020) used a propensity score matching method to correctly estimate the treatment delay effect when the time dependent confounder affects time to the treatment time, where the treatment delay effects is defined to the delay in treatment reception. In this paper, we proposed the Cox model based marginal structural model (Cox-MSM) framework to estimate the treatment delay effect and conducted extensive simulation studies to compare our proposed Cox-MSM with the propensity score matching method proposed by Hade et al. (2020). Our simulation results showed that the Cox-MSM leads to more exact estimate for the treatment delay effect compared with two sequential matching schemes based on propensity scores. Example from study in treatment discontinuation in conjunction with simulated data illustrates the practical advantages of the proposed Cox-MSM.

• Economic Analysis
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• v.27 no.2
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• pp.41-70
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• 2021

Using the 2017 and 2019 Survey on Immigrants' Living Conditions and Labour Force, we examine how the job training programs in Korea affect immigrants' labor market outcomes by applying the propensity score matching method. The results show that job training programs increase the probability of being employed by 6.4 percentage points and positively affect monthly wages. There is significant heterogeneity in the effects of job training effects across visa categories. For immigrants with work visas, the effect on the employment rate is relatively small, while the wage effect is considerably large. On the other hand, we do not find a positive wage effect for marriage migrants. Both the employment rate and the monthly wage increased through job training for permanent residents.