• Asian Pacific Journal of Cancer Prevention
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• v.17 no.6
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• pp.2841-2846
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• 2016

Background: Tobacco use is the single most important preventable risk factor for cancer. Surveillance of tobacco-related cancers (TRC) is critical for monitoring trends and evaluating tobacco control programmes. We analysed the trends of TRC and evaluated the population-based cancer registry (PBCR) in Delhi for simplicity, comparability, validity, timeliness and representativeness. Materials and Methods: We interviewed key informants, observed registry processes and analysed the PBCR dataset for the period 1988-2009 using the 2009 TRC definition of the International Agency for Research on Cancer. We calculated the percentages of morphologically verified cancers, death certificate-only (DCO) cases, missing values of key variables and the time between cancer diagnosis and registration or publication for the year 2009. Results: The number of new cancer cases increased from 5,854 to 15,244 (160%) during 1988-2009. TRC constituted 58% of all cancers among men and 47% among women in 2009. The age-adjusted incidence rates of TRC per 100,000 population increased from 64.2 to 97.3 among men, and from 66.2 to 69.2 among women during 1988-2009. Data on all cancer cases presenting at all major government and private health facilities are actively collected by the PBCR staff using standard paper-based forms. Data abstraction and coding is conducted manually following ICD-10 classifications. Eighty per cent of cases were morphologically verified and 1% were identified by death certificate only. Less than 1% of key variables had missing values. The median time to registration and publishing was 13 and 32 months, respectively. Conclusions: The burden of TRC in Delhi is high and increasing. The Delhi PBCR is well organized and generates high-quality, representative data. However, data could be published earlier if paper-based data are replaced by electronic data abstraction.

• The korean journal of orthodontics
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• v.51 no.5
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• pp.337-345
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• 2021

Objective: To investigate the phenotypes and predominant skeletodental pattern in pre-adolescent patients with Pierre-Robin sequence (PRS). Methods: The samples consisted of 26 Korean pre-adolescent PRS patients (11 boys and 15 girls; mean age at the investigation, 9.20 years) treated at the Department of Orthodontics, Seoul National University Dental Hospital between 1998 and 2019. Dental phenotypes, oral manifestation, cephalometric variables, and associated anomalies were investigated and statistically analyzed. Results: Congenitally missing teeth (CMT) were found in 34.6% of the patients (n = 9/26, 20 teeth, 2.22 teeth per patient) with 55.5% (n = 5/9) exhibiting bilaterally symmetric missing pattern. The mandibular incisors were the most common CMT (n = 11/20). Predominant skeletodental patterns included Class II relationship (57.7%), posteriorly positioned maxilla (76.9%) and mandible (92.3%), hyper-divergent pattern (92.3%), high gonial angle (65.4%), small mandibular body length to anterior cranial base ratio (65.4%), linguoversion of the maxillary incisors (76.9%), and linguoversion of the mandibular incisors (80.8%). Incomplete cleft palate (CP) of hard palate with complete CP of soft palate (61.5%) was the most frequently observed, followed by complete CP of hard and soft palate (19.2%) and CP of soft palate (19.2%) (p < 0.05). However, CP severity did not show a significant correlation with any cephalometric variables except incisor mandibular plane angle (p < 0.05). Five craniofacial and 15 extra-craniofacial anomalies were observed (53.8% patients); this implicated the need of routine screening. Conclusions: The results might provide primary data for individualized diagnosis and treatment planning for pre-adolescent PRS patients despite a single institution-based data.

• Journal of Gastric Cancer
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• v.14 no.4
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• pp.259-265
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• 2014

Purpose: Survival analysis of gastric cancer patients requires knowledge about factors that affect survival time. This paper attempted to analyze the survival of patients with incomplete registered data by using imputation methods. Materials and Methods: Three missing data imputation methods, including regression, expectation maximization algorithm, and multiple imputation (MI) using Monte Carlo Markov Chain methods, were applied to the data of cancer patients referred to the cancer institute at Imam Khomeini Hospital in Tehran in 2003 to 2008. The data included demographic variables, survival times, and censored variable of 471 patients with gastric cancer. After using imputation methods to account for missing covariate data, the data were analyzed using a Cox regression model and the results were compared. Results: The mean patient survival time after diagnosis was $49.1{\pm}4.4$ months. In the complete case analysis, which used information from 100 of the 471 patients, very wide and uninformative confidence intervals were obtained for the chemotherapy and surgery hazard ratios (HRs). However, after imputation, the maximum confidence interval widths for the chemotherapy and surgery HRs were 8.470 and 0.806, respectively. The minimum width corresponded with MI. Furthermore, the minimum Bayesian and Akaike information criteria values correlated with MI (-821.236 and -827.866, respectively). Conclusions: Missing value imputation increased the estimate precision and accuracy. In addition, MI yielded better results when compared with the expectation maximization algorithm and regression simple imputation methods.

• Journal of Periodontal and Implant Science
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• v.51 no.2
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• pp.124-134
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• 2021

Purpose: The aim of this study was to assess the association between the clinical status of rheumatoid arthritis (RA) and periodontitis (PD) in patients diagnosed with PD and to evaluate the impact of RA treatment on the severity of PD. Methods: The study included 148 participants with PD, of whom 64 were also diagnosed with RA (PD+RA group), while 84 age-matched participants were rheumatologically healthy (PD-only group). PD severity was assessed by the following periodontal parameters: clinical attachment loss, probing pocket depth (PPD), bleeding on probing (BOP), alveolar bone loss, and number of missing teeth. RA disease characteristics and impact of disease were evaluated by the Disease Activity Score 28 using C-reactive protein, disease duration, RA treatment, the RA Impact of Disease tool, and the Health Assessment Questionnaire. Outcome variables were compared using parametric and non-parametric tests and associations were evaluated using regression analysis with the calculation of odds ratios (ORs). Results: Participants in the PD+RA group had higher mean PPD values (2.81 ± 0.59 mm vs. 2.58 ± 0.49 mm, P=0.009) and number of missing teeth (6.27±4.79 vs. 3.93±4.08, P=0.001) than those in the PD-only group. A significant association was found between mean PPD and RA (OR, 2.22; 95% CI, 1.16-4.31; P=0.016). Within the PD+RA group, moderate to severe periodontal disease was significantly more prevalent among participants with higher RA disease activity (P=0.042). The use of biologic disease-modifying antirheumatic drugs (bDMARDs) was associated with a lower BOP percentage (P=0.016). Conclusions: In patients with PD, RA was associated with a higher mean PPD and number of missing teeth. The severity of PD was affected by the RA disease clinical activity and by treatment with bDMARDs, which were associated with a significantly lower mean BOP percentage.

• Water Engineering Research
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• v.5 no.1
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• pp.47-54
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• 2004

Several computer programs have been developed to make stochastically generated weather data from observed daily data. But they require fully dataset to run WGEN. Mostly, meterological data frequently have sporadic missing data as well as totally missing data. The modified WGEN has data filling algorithm for incomplete meterological datasets. Any other WGEN models have not the function of data filling. Modified WGEN with data filling algorithm is processing from the equation of Matalas for first order autoregressive process on a multi dimensional state with known cross and auto correlations among state variables. The parameters of the equation of Matalas are derived from existing dataset and derived parameters are adopted to fill data. In case of WGEN (Richardson and Wright, 1984), it is one of most widely used weather generators. But it has to be modified and added. It uses an exponential distribution to generate precipitation amounts. An exponential distribution is easier to describe the distribution of precipitation amounts. But precipitation data with using exponential distribution has not been expressed well. In this paper, generated precipitation data from WGEN and Modified WGEN were compared with corresponding measured data as statistic parameters. The modified WGEN adopted a formula of CLIGEN for WEPP (Water Erosion Prediction Project) in USDA in 1985. In this paper, the result of other parameters except precipitation is not introduced. It will be introduced through study of verification and review soon

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.2
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• pp.97-107
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• 2020

To analyze temporal and spatial changes in vegetation, it is necessary to determine the associated continuous distribution and conduct growth observations using time series data. For this purpose, the normalized difference vegetation index, which is calculated from optical images, is employed. However, acquiring images under cloud cover and rainfall conditions is challenging; therefore, time series data may often be unavailable. To address this issue, La et al. (2015) developed a multilinear simulation method to generate missing images on the target date using the obtained images. This method was applied to a small simulation area, and it employed a simple analysis of variables with lower constraints on the simulation conditions (where the environmental characteristics at the moment of image capture are considered as the variables). In contrast, the present study employs variables that reflect the growth characteristics of vegetation in a greater simulation area, and the results are compared with those of the existing simulation method. By applying the accumulated temperature, the average coefficient of determination (R²) and RMSE (Root Mean-Squared Error) increased and decreased by 0.0850 and 0.0249, respectively. Moreover, when data were unavailable for the same season, R² and RMSE increased and decreased by 0.2421 and 0.1289, respectively.

• The Journal of Information Systems
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• v.15 no.1
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• pp.73-96
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• 2006

Recently, ERP system is used as a important tool of management innovation for enterprise's survival and development. It is very important to recognize how much influence to organizational performance through ERP system implementation. The purpose of this study is to find out the impact of environmental, organizational, information systems characteristics on successful implementation of ERP systems in Korean SMEs(Small and Medium Enterprises). From previous researches on ERP adoption and implementation, 7 independent variables (competitiveness, government support, top management support, process innovation, project management, IS maturity, and ERP customizing), and 1 dependent variables (successful implementation of ERP systems) are identified. 3 questionnaires were removed from the study because of missing or inappropriate responses, and final samples are 91 SMEs. The results of hypothesis testing show that determinants of successful implementation of ERP systems are top management support and IS maturity. Five variables such as competitiveness, government support, process innovation, project management, and ERP customizing do not significantly influence to successful implementation of ERP systems. The contribution of this study is that it provides an empirical evidence of the causal relationship between ERP adoption factor and ERP success. This study showed that top management support and IS maturity are essential to accomplish successful ERP implementation for SMEs.

• Journal of Information Technology Applications and Management
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• v.29 no.2
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• pp.27-37
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• 2022

This study was attempted to identify the effects of physical and mental health on quality of life in middle-aged adults by gender. The Data were analyzed for 4,511 adults (2,260 men, 2,251 women) aged 45 to 65 who had no missing values in major variables based on the data of the 2016 Korea Health Panel. According to the data, the quality of life in middle-aged adults was .92 (±.08) for men and .91 (±.10) for women, which was significantly higher than that of women (t=3.54, p<.001). Factors affecting the quality of life in middle-aged men were subjective health status (β=.40, p<.001), stress (β=-.17, p<.001) and education level (β=.10, p<.001), and these variables explained 23% of the quality of life (F=227.28, p<.001). Factors affecting the quality of life in middle-aged women were subjective health status (β=.40, p<.001), stress (β=-.11, p<.001), education level (β=.05, p=.011) and anxiety (β=-.05, p=.022), and these variables explained 21% of the quality of life (F=145.42, p<.001). Based on the results of this study, the group with low level of education in middle-aged adults needs health management, education on how to relieve stress, and intensive management to improve the quality of life. In addition, the differentiated approach should be required to reduce anxiety in middle-aged women.

• Health Policy and Management
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• v.8 no.1
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• pp.52-96
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• 1998

To find causal factors and improvement plans of organizational commitment of public servants in urban health centers, a hypothetical causal model, which included 2 endogenous variables(organizational commitment & organizational satisfaction) and 15 exogenous variables, was constructed. Exogenous variables consisted of individual factors (sex, age, education, job-grade, and annual salary), psychological variables(pride for organization, extrinsic motivation, intrinsic motivation and support of supervisor) ad structural variables(formalization, centralization, communication, job-conflict, job-decision, and workload). In the hypothetical causal model, organizational commitment was supposed to be effect variable, and organizational satisfaction was presumed to be intervening variable to mediate between organizational commitment and exogenous variables. For data collection, cross-sectional self-administered questionnaire survey was conducted to 1,295 public servants from 32 urban health centers nationwide. The survey responses were from 934, 72.1% of subjects. But 756 responses(58.4%) were analyzed because of excluding ones with missing values. The hypothetical causal model was fitted by covariance structural analysis with maximum likelihood method. Main results were as follows: (1) The fitted causal model accounted for 33 and 55 percent of total variance of organizational commitment and organizational satisfaction of public servants, respectively. (2) In order of effect size, pride for organization, supervisor support, communication, extrinsic motivation and centralization had an indirect effect effect on organizational commitment through organizational satisfaction. However, the effect of centralization was negative. (3) Pride for organiztion, intrinsic motivation, organizational satisfaction, job-conflict, supervisor support, communication, age, centralization, annual salar and extrinsic motivation had indirect or direct effects on organizational commitment in order of effect size. Among them, effects of job-conflict and centraldization were negative. In conclusion, these results suggested that organizational commitment of public servants in urban health centers could be enhanced by pride for organization, intrinsic and extrinsic motivations, prevention of job-conflict and excess centralization, supervisor support and active communication. Especially, pride for organization and intrinsic motivation were expected to play the most important role.

• The Korean Journal of Applied Statistics
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• v.32 no.4
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• pp.543-559
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• 2019

Data often include missing values due to various reasons. If the missing data mechanism is not MCAR, analysis based on fully observed cases may an estimation cause bias and decrease the precision of the estimate since partially observed cases are excluded. Especially when data include many variables, missing values cause more serious problems. Many imputation techniques are suggested to overcome this difficulty. However, imputation methods using parametric models may not fit well with real data which do not satisfy model assumptions. In this study, we review imputation methods using nonlinear models such as kernel, resampling, and spline methods which are robust on model assumptions. In addition, we suggest utilizing imputation classes to improve imputation accuracy or adding random errors to correctly estimate the variance of the estimates in nonlinear imputation models. Performances of imputation methods using nonlinear models are compared under various simulated data settings. Simulation results indicate that the performances of imputation methods are different as data settings change. However, imputation based on the kernel regression or the penalized spline performs better in most situations. Utilizing imputation classes or adding random errors improves the performance of imputation methods using nonlinear models.