• Journal of Microbiology and Biotechnology
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• v.28 no.2
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• pp.338-346
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• 2018

Two molecular epidemiologic methods, IS6110 restriction fragment length polymorphism (IS6110-RFLP) and 24-locus mycobacterial interspersed repetitive unit-variable-number tandem repeat (MIRU-VNTR), are used worldwide in studies of Mycobacterium tuberculosis (MTB). Conversely, because of its poor resolution, pulsed-field gel electrophoresis (PFGE) is not widely used for MTB. In this study, we improved the 24-locus MIRU-VNTR and PFGE protocols and compared the effectiveness of these approaches for the molecular typing of MTB using 75 clinical isolates obtained from a cohort investigation of high-risk populations infected with MTB. The 24-locus MIRU-VNTR method demonstrated superior discriminatory ability, followed by PFGE and IS6110-RFLP. Next, we analyzed six isolates with clear epidemiologic connections; that is, isolates from patients who attended the same school. IS6110-RFLP and PFGE identified these samples as the same type. By contrast, according to MIRU-VNTR, two isolates differed from four other isolates at one locus each; one isolate was identified as Mtub29 and the other as QUB-26. In summary, the 24-locus MIRU-VNTR assay was the most useful molecular typing method among the three methods investigated due to its discriminatory power, short time required, and availability as an epidemiologic investigation tool. PFGE was the second-best method. Compared with the other loci assessed in the 24-locus MIRU-VNTR assay, the Mtub29 and QUB-26 loci appeared to exhibit greater variability during transmission.

• Journal of Preventive Medicine and Public Health
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• v.33 no.2
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• pp.199-207
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• 2000

Objectives : This study was peformed to identify the risk factors related to the development of hypertension in a rural area. Method : Total of 3,573 subjects in Chung-Song County were interviewed and examined in 1996. The study cohort comprised 2,580 hypertension-free subjects aged above 20. One-year follow up was completed for 1,781 subjects(69.0%) in 1997. General characteristics(age, gender, education level, economic status, marital status), the family history of hypertension, diet, alcohol, smoking, coffee, stress, past history of oral contraceptive and menopausal status in female, height, weight, waist and hip circumference, baseline blood pressure, and serum total cholesterol were considered as risk factors. Results : Multivariate analysis using logistic regression model indicated that age(RH=1.50, 95% CI; 1.15-1.96), the family history of hypertension(RR=2.11, 95% CI; 1.04-4.26), waist-hip ratio(WHR) (RR=2.09, 95%, CI; 1.15-3.79), and baseline systolic blood pressure(130-139/<120mmHg)(RR=3.34, 95% CI; 1.47-7.60) were significant risk factors associated with the development of hypertension above the borderline level in male. In female, age(RR=1.06, 95% CI; 1.03-1.09), change in menopausal status$(no{\rightarrow}yes/no{\rightarrow}no)$ (RR=3.32, 95% CI; 1.01-10.87), baseline systolic blood pressure(120-129/<120mmHg: RR=2.00, 95% CI; 1.02-3.90)(130-139/<120mmHg: RR=2.64, 95% CI; 1.34-5.20) and baseline diastolic blood pressure(85-89/<80mmHg)(RR=4.09, 95% CI; 1.86-8.96) were identified as risk factors. Conclusions : Age and high normal blood pressure were significant risk factors for the development of hypertension above the borderline level. In addition, the family history of hypertension and WHR in men, and the change of menopausal status in women might be significant risk factors in Korea.

• Journal of Environmental Science International
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• v.22 no.2
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• pp.151-161
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• 2013

The assessment of personal exposure is a critical component in population-based epidemiologic studies of air pollution. This study was conducted to apply and compare the four exposure estimation methods of individual-level to air pollution concentration in a cohort including 2,283 subjects in Gwangyang, Korea. Individual-level exposure of air pollution were estimated using multiple approaches, including average across all monitors, nearest monitor, and spatial interpolation by inverse distance weighting and kriging. The mean concentrations of $PM_{10}$, $NO_2$, $SO_2$, CO, $O_3$ by four exposure estimation methods were slightly different but not significantly different from each other. Cross-validation showed that kriging was more accurate than other exposure estimation methods because kriging has probably predicted individual exposure levels equivalent to residential locations after estimating the parameters of a model according to the spatial surface of air pollution concentration. These data support that spatial interpolation methods may provide better estimates than selecting the value from the nearest monitor and averaging across values from all monitors by reflecting spatial attributes of air pollution on personal level.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.67-75
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• 2018

Multidimensional scaling (MDS) is an exploratory analysis of multivariate data to represent the dissimilarity among objects in the geometric low-dimensional space. However, a general MDS map only shows the information of objects without any information about variables. In this study, we used MDS based on the algorithm of Torgerson (Theory and Methods of Scaling, Wiley, 1958) to visualize some clusters of objects in categorical data. For this, we convert given data into a multiple indicator matrix. Additionally, we added the information of levels for each categorical variable on the MDS map by applying the partition method of Shin et al. (Korean Journal of Applied Statistics, 28, 1171-1180, 2015). Therefore, we can find information on the similarity among objects as well as find associations among categorical variables using the proposed MDS map.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.5
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• pp.2213-2218
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• 2012

Introduction: Worldwide, breast cancer is the commonest cause of cancer death in women. However, the survival rate varies across regions at averages of 73%and 57% in the developed and developing countries, respectively. Objective: This study aimed to determine the survival rate of breast cancer among the women of Malaysia and characteristics of the survivors. Method: A retrospective cohort study was conducted on secondary data obtained from the Breast Cancer Registry and medical records of breast cancer patients admitted to Hospital Kuala Lumpur from 2005 to 2009. Survival data were validated with National Birth and Death Registry. Statistical analysis applied logistic regression, the Cox proportional hazard model, the Kaplan-Meier method and log rank test. Results: A total of 868 women were diagnosed with breast cancer between January 2005 and December 2009, comprising 58%, 25% and 17% Malays, Chinese and Indians, respectively. The overall survival rate was 43.5% (CI 0.573-0.597), with Chinese, Indians and Malays having 5 year survival rates of 48.2% (CI 0.444-0.520), 47.2% (CI 0.432-0.512) and 39.7% (CI 0.373-0.421), respectively (p<0.05). The survival rate was lower as the stages increased, with the late stages were mostly seen among the Malays (46%), followed by Chinese (36%) and Indians (34%). Size of tumor>3.0cm; lymph node involvement, ERPR, and HER 2 status, delayed presentation and involvement of both breasts were among other factors that were associated with poor survival. Conclusions: The overall survival rate of Malaysian women with breast cancer was lower than the western figures with Malays having the lowest because they presented at late stage, after a long duration of symptoms, had larger tumor size, and had more lymph nodes affected. There is an urgent need to conduct studies on why there is delay in diagnosis and treatment of breast cancer women in Malaysia.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.14
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• pp.5883-5888
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• 2014

Background: Breast cancer is the most common cancers in female populations. The exact cause is not known, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM) is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificial neural network (ANN) models have been increasingly applied to predict survival data. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. Materials and Methods: A historical cohort study was established with 104 patients suffering from BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas under the receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzed using R statistical software. Results: The AUC for the first, second and third years after diagnosis are 0.918, 0.780 and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statistically higher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between the performance in terms of prediction by ANN and LLM. Conclusions: This study demonstrated that the ability of prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction of survival in field of breast cancer is suggested.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.2
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• pp.1107-1110
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• 2013

Intrahepatic cholangiocarcinoma (ICC), one of the primary liver cancers, is frequent in the northeastern part of Thailand. Surgical resection remains the best method of treatment, but patients suffering from ICC usually present at a late stage of the disease. Studies of survival and prognostic factors after surgery remain rare. The aim here was to evaluate the survival rate and factors affecting the survival of patients with intrahepatic cholangiocarcinoma after surgery. The study used a retrospective cohort design. The subjects were 73 consecutive patients with ICC, who were admitted for surgery to Srinagarind Hospital, Khon Kaen University, during the period 2005-2009. The censoring date was 31 December, 2011, data being evaluated using uni- and multivariate analyses. Postoperative survival analysis was performed by the Kaplan-Meier method, and the Cox proportional hazard model was used to identify independent prognostic factors. The total follow-up time was 99 person-years. The total number of deaths was 59, giving a mortality rate of 59 per 100 person-years. The cumulative 1-, 3-, and 5-year survival rates were 52.1%, 21.7%, and 11.2%, respectively. The median duration of survival after resection was 12.4 months. Univariate analysis revealed stage of disease, lymph node metastasis, histological type, histological grade and macroscopic classification to be statistically significant (p-value<0.05) prognostic factors. In the multivariate analysis, only macroscopic classification was statistically significant (p-value<0.05). In conclusion, macroscopic classification was the only independent factor found to be significantly associated with survival following surgical treatment of ICC.

• Communications for Statistical Applications and Methods
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• v.29 no.4
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• pp.421-439
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• 2022

Phase I dose-finding trials are essential in drug development. By finding the maximum tolerated dose (MTD) of a new drug or treatment, a Phase I trial establishes the recommended doses for later-phase testing. The primary toxicity endpoint of interest is often a binary variable, which describes an event of a patient who experiences dose-limiting toxicity. However, there is a growing interest in dose-finding studies regarding non-binary outcomes, defined by either the weighted sum of rates of various toxicity grades or a continuous outcome. Although several novel methods have been proposed in the literature, accessible software is still lacking to implement these methods. This study introduces a newly developed R package, UnifiedDoseFinding, which implements three phase I dose-finding methods with non-binary outcomes (Quasi- and Robust Quasi-CRM designs by Yuan et al. (2007) and Pan et al. (2014), gBOIN design by Mu et al. (2019), and by a method by Ivanova and Kim (2009)). For each of the methods, UnifiedDoseFinding provides corresponding functions that begin with next that determines the dose for the next cohort of patients, select, which selects the MTD defined by the non-binary toxicity endpoint when the trial is completed, and get oc, which obtains the operating characteristics. Three real examples are provided to help practitioners use these methods. The R package UnifiedDoseFinding, which is accessible in R CRAN, provides a user-friendly tool to facilitate the implementation of innovative dose-finding studies with nonbinary outcomes.

• Journal of Korean Academy of Nursing
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• v.52 no.6
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• pp.564-581
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• 2022

Purpose: This study aimed to evaluate the validity and reliability of the Korean version of the Readiness for Practice Survey (K-RPS). Method: The English Readiness for Practice Survey was translated into Korean using the Translation, Review, Adjudication, Pretesting, and Documentation (TRAPD) method. Secondary data analysis was performed using the dataset from the New Nurse e-Cohort study (Panel 2020) in South Korea. This study used a nationally representative sample of 812 senior nursing students. Exploratory and confirmatory factor analyses were also conducted. Convergent validity within the items and discriminant validity between factors were assessed to evaluate construct validity. Construct validity for hypothesis testing was evaluated using convergent and discriminant validity. Ordinary α was used to assess reliability. Results: The K-RPS comprises 20 items examining four factors: clinical problem solving, learning experience, professional responsibilities, and professional preparation. Although the convergent validity of the items was successfully verified, discriminant validity between the factors was not. The K-RPS construct validity was verified using a bi-factor model (CMIN/DF 2.20, RMSEA .06, TLI .97, CFI .97, and PGFI .59). The K-RPS was significantly correlated with self-esteem (r = .43, p < .001) and anxiety about clinical practicum (r = - .50, p < .001). Internal consistency was reliable based on an ordinary α of .88. Conclusion: The K-RPS is both valid and reliable and can be used as a standardized Korean version of the Readiness for Practice measurement tool.

• Archives of Plastic Surgery
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• v.50 no.3
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• pp.254-263
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• 2023

Background The three-dimensional (3D) evaluation of skeletal stability after orthognathic surgery is a time-consuming and complex procedure. The complexity increases further when evaluating the surgery-first orthognathic approach (SFOA). Herein, we propose and validate a simple time-saving method of 3D analysis using a single software, demonstrating high accuracy and repeatability. Methods This retrospective cohort study included 12 patients with skeletal class 3 malocclusion who underwent bimaxillary surgery without any presurgical orthodontics. Computed tomography (CT)/cone-beam CT images of each patient were obtained at three different time points (preoperation [T0], immediately postoperation [T1], and 1 year after surgery [T2]) and reconstructed into 3D images. After automatic surface-based alignment of the three models based on the anterior cranial base, five easily located anatomical landmarks were defined to each model. A set of angular and linear measurements were automatically calculated and used to define the amount of movement (T1-T0) and the amount of relapse (T2-T1). To evaluate the reproducibility, two independent observers processed all the cases, One of them repeated the steps after 2 weeks to assess intraobserver variability. Intraclass correlation coefficients (ICCs) were calculated at a 95% confidence interval. Time required for evaluating each case was recorded. Results Both the intra- and interobserver variability showed high ICC values (more than 0.95) with low measurement variations (mean linear variations: 0.18 mm; mean angular variations: 0.25 degree). Time needed for the evaluation process ranged from 3 to 5 minutes. Conclusion This approach is time-saving, semiautomatic, and easy to learn and can be used to effectively evaluate stability after SFOA.