• Korean Journal of Clinical Pharmacy
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• v.29 no.2
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• pp.109-114
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• 2019

Background: Rheumatoid arthritis (RA) is a systemic inflammatory disease that manifests as joint damage or athletic disability via sustained inflammation of the synovial membrane. The risk of cardiovascular disease (CVD) is higher in RA patients. This study aimed at evaluating the association between CVD comorbidities and RA by comparing a pharmacotherapy group with a non-pharmacotherapy group. Methods: Patient sample data from the Health Insurance Review and Assessment Service (HIRA-NPS-2016) were used. Inverse probability of treatment weighting (IPTW) using the propensity score was used to minimize the differences in patient characteristics. Logistic regression analysis was used to evaluate the risk of CVD comorbidities. Results: The analyses included 1,207,213 patients, of which 33,122 (2.8%) had RA. The odds ratios (OR) of CVD comorbidities were increased in RA patients; ischemic heart disease (IHD: OR 1.75; 95% CI 1.73, 1.77), cerebral infarction (CERI: OR 1.28; 95% CI 1.26, 1.30), hypertension (HTN: OR 1.44; 95% CI 1.43, 1.45), diabetes mellitus (DM: OR 2.04; 95% CI 2.03, 2.06), and dyslipidemia (DL: OR 3.49; 95% CI 3.47, 3.51). The ORs of IHD, CERI, HTN, and DM in the traditional DMARD and biologic treatment groups were decreased, compared with those in the non-pharmacotherapy group. Conclusions: Thus, CVD risk was higher in RA patients, considering age, sex, and socioeconomic status. Appropriate pharmacotherapy could decrease the risk of CVD comorbidities in RA patients.

• 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.

• 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.

• 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.

• Educational Technology International
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• v.25 no.1
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• pp.67-92
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• 2024

This study aims to verify the practical effectiveness of learning management system (LMS) by introducing a LMS enhancing digital assessment utilizing automatic item generation in order to strengthen college student's mathematics performance. Teaching assisted with digital assessment in the LMS was applied to college mathematics classes, and the research question is whether or not students in the classes utilizing the LMS perform better than the regular classes. In particular, a calculus course, which is the foundation of important artificial intelligence technology in the future, was utilized in this study. The participants of this study were 248 freshmen in science and engineering who were taking calculus courses at a small to mid-size university. A total of 156 freshmen were selected after applying a propensity score matching method (PSMM), 78 from classes utilizing the LMS and 78 from regular classes without the LMS assisted with the digital assessment. As a result, it was found that there was a statistically significant difference in the math academic growth of students who used the LMS and those who did not. In other words, when LMS was used in calculus, students' academic growth was greater. The results of this study are meaningful in that they observed students' academic growth and confirmed that LMS enables a positive role in students' academic growth. In addition, if digital assessment is strengthened and LMS that enables individualized learning analysis is introduced and implemented in educational institutions, it is expected to play a major role in strengthening students' academic performance.

• Communications for Statistical Applications and Methods
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• v.23 no.1
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• pp.1-20
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• 2016

Causal inference methodologies have been developed for the past decade to estimate the unconfounded effect of an exposure under several key assumptions. These assumptions include, but are not limited to, the stable unit treatment value assumption, the strong ignorability of treatment assignment assumption, and the assumption that propensity scores be bounded away from zero and one (the positivity assumption). Of these assumptions, the first two have received much attention in the literature. Yet the positivity assumption has been recently discussed in only a few papers. Propensity scores of zero or one are indicative of deterministic exposure so that causal effects cannot be defined for these subjects. Therefore, these subjects need to be removed because no comparable comparison groups can be found for such subjects. In this paper, using currently available causal inference methods, we evaluate the effect of arbitrary cutoffs in the distribution of propensity scores and the impact of those decisions on bias and efficiency. We propose a tree-based method that performs well in terms of bias reduction when the definition of positivity is based on a single confounder. This tree-based method can be easily implemented using the statistical software program, R. R code for the studies is available online.

• Korean Journal of Clinical Pharmacy
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• v.26 no.1
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• pp.46-52
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• 2016

Background: The use of acid suppressive agents became a standard therapy in an intensive care unit (ICU) to prevent stress related gastrointestinal mucosal damage. However, the risk of infectious diseases has been concerned. Objective: The study was to determine the differences between histamine 2 receptor antagonists (H2RA) and proton pump inhibitors (PPI) in incidence of nosocomial pneumonia and pseudomembranous colitis (PMC) by Clostridium difficile with patients in ICU. Methods: This is a retrospective comparative study including patients admitted to the ICU who were at least 18 years of age and stayed for more than 48hrs from August 1, 2014 to January 31, 2015. The propensity score analysis and propensity matched multivariable logistic regression were used in analyzing data to control for confounders. Results: A total of 155 patients were assessed. H2RA were prescribed in 110 (53.9%) and PPI were in 45 (22.1%). Nosocomial pneumonia developed in 37 (23.9%); 25 (22.7%) were on H2RA and 12 (26.7%) were on PPI. The unadjusted incidence of nosocomial pneumonia was slightly higher in the patients with PPI (odds ratio (OR) 1.24; 95% confidence interval (CI): 0.54-2.71) compared to them with H2A. After adjusting with propensity score, the adjusted OR with PPI was 1.35 (95% CI: 0.44-4.11). The propensity score matched analyses showed similar results. Conclusion: The uses of PPI and H2RA as a stress ulcer prophylaxis agent showed similarity in the incidence of nosocomial pneumonia and PMC.

• 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.28 no.1
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• pp.109-117
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• 2017

This study aims to identify the effect for exercise intensity on hypertension using propensity score based on the sixth Korea National Health and Nutrition Examination Survey data and to provide an evidence for the most effective exercise intensity for prevention or treatment of hypertension. Specifically, we select 3,486 subjects who aged between 18 and 65 years after excluding some subjects who are expected to have limited athletic ability. We estimate propensity scores for exercise intensity based on the confounders such as sex, age, smoking, drinking, and natrium intake. Considering the complex survey design, we conduct a descriptive analysis and multiple logistic regression for hypertension with propensity score as a covariate. Although the results of the study did not show statistically significant relationship between exercise intensity and hypertension, we expect that it can be used as a basis evidence that the appropriate exercise of moderate intensity may be more effective for the prevention and treatment of hypertension rather than strong intensity exercise and non-exercise.

• The Korean Journal of Applied Statistics
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• v.30 no.1
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• pp.69-82
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• 2017

R-indicator (representativeness indicator) is used to check the representativeness of samples when non-responses occur. The representativeness is related with the accuracy of parameter estimator and the accuracy is related with bias of the estimator. Hence, unbiased estimator generates high accuracy. Therefore, high value of R-indicator guarantees the accuracy of parameter estimation with a small bias. R-indicator is calculated through propensity scores obtained by logit or probit modeling. In this paper we investigate the degree of relation between R-indicator and different non-response rates in strata using simulation studies. We also analyze a modified Korea Economic Census data for real data analysis.