• Investigative Magnetic Resonance Imaging
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• v.25 no.2
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• pp.118-128
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• 2021

Purpose: To investigate the value of MR textural analysis, including use of diffusion-weighted imaging (DWI) to differentiate malignant from benign soft-tissue tumors on 3T MRI. Materials and Methods: We enrolled 69 patients (25 men, 44 women, ages 18 to 84 years) with pathologically confirmed soft-tissue tumors (29 benign, 40 malignant) who underwent pre-treatment 3T-MRI. We calculated MR texture, including mean, standard deviation (SD), skewness, kurtosis, mean of positive pixels (MPP), and entropy, according to different spatial-scale factors (SSF, 0, 2, 4, 6) on axial T1- and T2-weighted images (T1WI, T2WI), contrast-enhanced T1WI (CE-T1WI), high b-value DWI (800 sec/mm²), and apparent diffusion coefficient (ADC) map. We used the Mann-Whitney U test, logistic regression, and area under the receiver operating characteristic curve (AUC) for statistical analysis. Results: Malignant soft-tissue tumors had significantly lower mean values of DWI, ADC, T2WI and CE-T1WI, MPP of ADC, and CE-T1WI, but significantly higher kurtosis of DWI, T1WI, and CE-T1WI, and entropy of DWI, ADC, and T2WI than did benign tumors (P < 0.050). In multivariate logistic regression, the mean ADC value (SSF, 6) and kurtosis of CE-T1WI (SSF, 4) were independently associated with malignancy (P ≤ 0.009). A multivariate model of MR features worked well for diagnosis of malignant soft-tissue tumors (AUC, 0.909). Conclusion: Accurate diagnosis could be obtained using MR textural analysis with DWI and CE-T1WI in differentiating benign from malignant soft-tissue tumors.

• Journal of the Korean Chemical Society
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• v.47 no.1
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• pp.19-25
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• 2003

A spectrophotometric method for the simultaneous determination of anionic and nonionic surfactant based on the application of multivariate calibration method such as principal component regression(PCR) and partial least squares(PLS) has been studied. The calibration models in PCR and PLS were obtained from the spectral data in the range of 400~700 nm for each standard of a calibration set of 26 standards, each containing different amounts of two surfactants. The relative standard error of prediction(RSEP$_{\alpha}$) was obtained to assess the model goodness in quantifying each analyte in a 5 validation samples which containing different amounts of two surfactants.

• Journal of the Korean Society of School Health
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• v.35 no.3
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• pp.133-142
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• 2022

Purpose: The purpose of the study was to investigate the effect of the environment on adolescent binge drinking. Methods: The study was designed as a cross-sectional study. Using statistics from the 17th (20201) Korea Youth Risk Behavior Web-based Survey, the raw data target population was 2,629,588 people, and the sample group used for analysis as the final data was 54,848 people. A Rao-scott 𝑥² test and univariate multinomial logistic regression analysis were performed using IBM SPSS 27.0. Results: In the results of univariate logistic regression analysis and multivariate logistic regression analysis, common related variables were gender, school level, academic achievement, sleep satisfaction, current smoking, daily smoking, and alcohol education experience. Conclusion: As a result of confirming the factors influencing binge drinking in Korean adolescents, some variables that increase the possibility of problematic drinking behavior in the socio-environmental areas such as individuals, communities, and national policies were identified. For effective prevention and intervention, it is necessary to develop programs to build a healthy environmental support system with support from national policies, including individuals, peer groups, and communities.

• The Korean Journal of Applied Statistics
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• v.35 no.3
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• pp.395-406
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• 2022

This paper studies the outlier detection method for multivariate long memory time series. The existing outlier detection methods are based on a short memory VARMA model, so they are not suitable for multivariate long memory time series. It is because higher order of autoregressive model is necessary to account for long memory, however, it can also induce estimation instability as the number of parameter increases. To resolve this issue, we propose outlier detection methods based on the VHAR structure. We also adapt the robust estimation method to estimate VHAR coefficients more efficiently. Our simulation results show that our proposed method performs well in detecting outliers in multivariate long memory time series. Empirical analysis with stock index shows RVHAR model finds additional outliers that existing model does not detect.

• Communications for Statistical Applications and Methods
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• v.22 no.1
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• pp.41-54
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• 2015

In this article, we propose nonconcave penalties on a reduced-rank regression model to select variables and estimate coefficients simultaneously. We apply HARD (hard thresholding) and SCAD (smoothly clipped absolute deviation) symmetric penalty functions with singularities at the origin, and bounded by a constant to reduce bias. In our simulation study and real data analysis, the new method is compared with an existing variable selection method using $L_1$ penalty that exhibits competitive performance in prediction and variable selection. Instead of using only one type of penalty function, we use two or three penalty functions simultaneously and take advantages of various types of penalty functions together to select relevant predictors and estimation to improve the overall performance of model fitting.

• Annals of Geriatric Medicine and Research
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• v.22 no.4
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• pp.184-188
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• 2018

Background: The risk factors of nocturia in older adults remain unclear. We aimed to investigate factors associated with nocturia using the National Health and Nutrition Examination Survey (NHANES) data. Methods: Among 40,790 participants, 4,698 participants aged ${\geq}65$ years were included from the NHANES dataset between 2005 and 2012. A multivariate logistic regression analysis was performed to determine the odds ratio (OR) for nocturia. A subgroup analysis was conducted based on sex and underlying diseases. Results: In the multivariate logistic regression model, obesity (OR, 1.46; 95% confidence interval [CI], 1.28-1.68), hypertension (OR, 1.28; 95% CI, 1.07-1.52), and diabetes mellitus (DM) (OR, 1.27; 95% CI, 1.11-1.45) were significantly associated with nocturia. These factors were associated with nocturia regardless of sex. In a subgroup of participants with hypertension, obesity (OR, 1.44; 95% CI, 1.25-1.67) and DM (OR, 1.26; 95% CI, 1.09-1.45) were associated with nocturia. In the additional analysis on patients with DM, nocturia was associated with obesity (OR, 1.33; 95% CI, 1.06-1.67) and duration of DM (OR, 1.02; 95% CI, 1.01-1.03). Conclusion: This study demonstrated that hypertension, DM, and obesity were significantly associated with the prevalence of nocturia in older adult patients regardless of sex. In particular, obesity was associated with nocturia in every subgroup analysis.

• Journal of Trauma and Injury
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• v.35 no.1
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• pp.25-33
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• 2022

Purpose: The scoring system for traumatic liver injury (SSTLI) was developed in 2015 to predict mortality in patients with polytraumatic liver injury. This study aimed to validate the SSTLI as a prognostic factor in patients with polytrauma and liver injury through a generalized estimating equation analysis. Methods: The medical records of 521 patients with traumatic liver injury from January 2015 to December 2019 were reviewed. The primary outcome variable was in-hospital mortality. All the risk factors were analyzed using multivariate logistic regression analysis. The SSTLI has five clinical measures (age, Injury Severity Score, serum total bilirubin level, prothrombin time, and creatinine level) chosen based on their predictive power. Each measure is scored as 0-1 (age and Injury Severity Score) or 0-3 (serum total bilirubin level, prothrombin time, and creatinine level). The SSTLI score corresponds to the total points for each item (0-11 points). Results: The areas under the curve of the SSTLI to predict mortality on post-traumatic days 0, 1, 3, and 5 were 0.736, 0.783, 0.830, and 0.824, respectively. A very good to excellent positive correlation was observed between the probability of mortality and the SSTLI score (γ=0.997, P<0.001). A value of 5 points was used as the threshold to distinguish low-risk (<5) from high-risk (≥5) patients. Multivariate analysis using the generalized estimating equation in the logistic regression model indicated that the SSTLI score was an independent predictor of mortality (odds ratio, 1.027; 95% confidence interval, 1.018-1.036; P<0.001). Conclusions: The SSTLI was verified to predict mortality in patients with polytrauma and liver injury. A score of ≥5 on the SSTLI indicated a high-risk of post-traumatic mortality.

• Journal of Preventive Medicine and Public Health
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• v.56 no.1
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• pp.21-30
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• 2023

Objectives: To prevent the spread of coronavirus disease 2019 (COVID-19), behaviors such as mask-wearing, social distancing, decreasing mobility, and avoiding crowds have been suggested, especially in high-risk countries such as Indonesia. Unfortunately, the level of compliance with those practices has been low. This study was conducted to determine the predisposing, enabling, and reinforcing factors of COVID-19 prevention behavior in Indonesia. Methods: This cross-sectional study used a mixed-methods approach. The participants were 264 adults from 21 provinces in Indonesia recruited through convenience sampling. Data were collected using a Google Form and in-depth interviews. Statistical analysis included univariate, bivariate, and multivariate logistic regression. Furthermore, qualitative data analysis was done through content analysis and qualitative data management using Atlas.ti software. Results: Overall, 44.32% of respondents were non-compliant with recommended COVID-19 prevention behaviors. In multivariate logistic regression analysis, low-to-medium education level, poor attitude, insufficient involvement of leaders, and insufficient regulation were also associated with decreased community compliance. Based on in-depth interviews with informants, the negligence of the Indonesian government in the initial stages of the COVID-19 pandemic may have contributed to the unpreparedness of the community to face the pandemic, as people were not aware of the importance of preventive practices. Conclusions: Education level is not the only factor influencing community compliance with recommended COVID-19 prevention behaviors. Changing attitudes through health promotion to increase public awareness and encouraging voluntary community participation through active risk communication are necessary. Regulations and role leaders are also required to improve COVID-19 prevention behavior.

• Information Systems Review
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• v.22 no.1
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• pp.73-89
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• 2020

This study started to identify the "Forward-looking" of the technology appraisal model introduced to diversify financing methods of SMEs and improve financial accessibility. The multivariate regression analysis was performed by setting the business performance(growth, profitability, and stability) of technology financing companies as dependent variables, technology appraisal items as independent variables, number of employees, age of the company, asset and the Korea Standard of Industry Classification related to firm size and industry characteristics as control variables. As a result of the analysis, the technology appraisal items did not explain the profitability of the company significantly and had a limited explanatory power on growth potential. However, in terms of stability, we confirmed that R&D capacity is a significant variable explaining the debt ratio of technology financing companies. Therefore, it is concluded that the 'Forward-looking' reflection on the growth and profitability of the company should be strengthened in the future adjustment of the technology appraisal model and the development of the technology appraisal model for investment.

• Korea Journal of Hospital Management
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• v.25 no.3
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• pp.67-77
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• 2020

Background: As of 2016, average Koreans sleep 7 hours and 42 minutes, the lowest figure among Organization for Economic Cooperation and Development(OECD) countries, and the number of people with sleep disorders reached 561,000. Accordingly, the government has promoted the provision of 'Multiple Sleep Test' to strengthen the diagnosis service for patients with 'sleep disorder' in july 2018. As a result, healthcare costs for patients with sleep disorder is on the rise every year. In this study, we utilized 'Appropriate Sleep' criteria of United States's National Sleep Foundation(NSF) then investigated Korean's sleep pertinence using ｢7th National Health and Nutrition Survey for 2016-2018」 by different occupational type, demographic characteristics, socio-economic characteristics, and health behaviors. Methods: We performed descriptive analysis to examine differences of sleep appropriateness by various sample characteristics. Multivariate logistic regression models were used to examine sleep appropriateness by occupational type and other variables. We also analyzed subgroup models to investigate. Results: As a result, a total of 1,948 (18.37%) study subjects experienced in-appropriate sleep. Results of the Multivariate logistic regression analysis revealed that blue color group had a higher odds ratio (OR) for experiencing in-appropriate sleep (OR=1.179). In addition, the odds ratio of experienced in-appropriate sleep among the elderly aged 70 and over was 2.698, and the odds ratio of the overstressed group was 1.299. Furthermore, sub-group analysis showed that blue color job of female(Or=1.334), high school or below(OR=1.404), divorce/death/separation(OR=2.039), 25%ile-50%lie income group(OR=1.411) more likely experienced in-appropriate sleep. Conclusion: Growing sleep disorder patients and related health care costs are expected. Government should apply detailed 'total periodic sleep disorder management policy' including pre-consultation, examination, diagnosis, treatment, post-consultation, self-management especially to vulnerable population that this study found.