• Health Policy and Management
• /
• v.19 no.4
• /
• pp.98-120
• /
• 2009

The purpose of this study is to elicit preference for drug listing decision criteria and to estimate the ICER threshold in South Korea using the discrete choice experiment (DCE) method. To collect the data, a DCE survey was administered to a subject sample either educated in the principle concepts of pharmacoeconomics or were decision makers within that field. Subjects chose between alternative drug profiles differing in four attributes: ICER, uncertainty, budget impact and severity of disease. The orthogonal and balanced designs were determined through computer algorithm to take the optimal set of drug profiles. The survey employed 15 hypothetical choice sets. A random effect probit model was used to analyze the relative importance of attributes and the probabilities of a recommendation response. Parameter estimates from the models indicated that three attributes (ICER, Impact, Severity of disease) influenced respondents' choice significantly(p${\pm}$0.001). In addition, each parameter displayed an expected sign. The Lower the ICER, the higher the probability of choosing that alternative. Respondents also preferred low levels of uncertainty and smaller impact on health service budget. They were also more likely to choose drugs for serious diseases rather than mild or moderate ones. Uncertainty however is not statistically significant. The ICER threshold, at which the probability of a recommendation was 0.5, was 29,000,000 KW/QALY in expert group and 46,500,000 KW/QALY in industry group. We also found that those in our sample were willing to accept high ICER to get medication for severe diseases. This study demonstrates that the cost-effectiveness, budget impact and severity of disease are the main reimbursement decision criteria in South Korea, and that DCE can be a useful tool in analyzing the decision making process where a variety of factors are considered and prioritized.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.1343-1346
• /
• 2005

In the cold rolling mill, coil breakage that generated in rolling process makes the various types of troubles such as the degradation of productivity and the damage of equipment. Recent researches were done by the mechanical analysis such as the analysis of roll chattering or strip inclining and the prevention of breakage that detects the crack of coil. But they could cover some kind of breakages. The prediction of Coil breakage was very complicated and occurred rarely. We propose to build effective prediction modes for coil breakage in rolling process, based on data mining model. We proposed three prediction models for coil breakage: (1) decision tree based model, (2) regression based model and (3) neural network based model. To reduce model parameters, we selected important variables related to the occurrence of coil breakage from the attributes of coil setup by using the methods such as decision tree, variable selection and the choice of domain experts. We developed these prediction models and chose the best model among them using SEMMA process that proposed in SAS E-miner environment. We estimated model accuracy by scoring the prediction model with the posterior probability. We also have developed a software tool to analyze the data and generate the proposed prediction models either automatically and in a user-driven manner. It also has an effective visualization feature that is based on PCA (Principle Component Analysis).

• Journal of Korean Society for Quality Management
• /
• v.41 no.1
• /
• pp.149-162
• /
• 2013

Purpose: The purpose of this study was to design of a probabilistic model for optimum manpower planning in R&D department by Montecarlo simulation. Methods: We investigate the process and the requirement of manpower planning and scheduling in R&D department. The empirical distributions of necessary time and manpower for R&D projects are developed. From the empirical distributions, we can estimate a probability distribution of optimum manpower in R&D department. A simulation method of estimating the probability distribution of optimum manpower is considered. It is a useful tool for obtaining the sum, the variance and other statistics of the distributions. Results: The real industry cases are given and the properties of the model are investigated by Montecarlo Simulation. we apply the model to the research laboratory of the global company, and investigate and compensate the weak points of the model. Conclusion: The proposed model provides various and correct information such as average, variance, percentile, minimum, maximum and so on. A decision maker of a company can easily develop the future plan and the task of researchers may be allocated properly. we expect that the productivity can be improved by this study. The results of this study can be also applied to other areas including shipbuilding, construction, and consulting areas.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.23 no.4
• /
• pp.16-41
• /
• 2020

Geospatial input setting to represent the reality of spatial distribution or quantitative property within model has become a major interest in earth system simulation. Many studies showed the variation of grid resolution could lead to drastic changes of spatial model results because of insufficient surface property estimations. Hence, in this paper, the authors proposed Monte Carlo Integration (MCI) to apply spatial probability (SP) in a spatial-sampling framework using a three-dimensional point cloud (3DPC) to keep the optimized spatial distribution and area/volume property of buildings in urban area. Three different decision rule based building identification results were compared : SP threshold, cell size, and 3DPC density. Results shows the identified building area property tend to increase according to the spatial sampling grid area enlargement. Hence, areal building property manipulation in the sampling frameworks by using decision rules is strongly recommended to increase reliability of geospatial modeling and analysis results. Proposed method will support the modeling needs to keep quantitative building properties in both finer and coarser grids.

• Physical Therapy Rehabilitation Science
• /
• v.11 no.3
• /
• pp.285-295
• /
• 2022

Objective: The purpose of this study is to use logistic regression and decision tree analysis to identify the factors that affect the success or failurein the national physical therapy examination; and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 76,727 subjects from the physical therapy national examination data provided by the Korea Health Personnel Licensing Examination Institute. The target variable was pass or fail, and the input variables were gender, age, graduation status, and examination area. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In the logistic regression analysis, subjects in their 20s (Odds ratio, OR=1, reference), expected to graduate (OR=13.616, p<0.001) and from the examination area of Jeju-do (OR=3.135, p<0.001), had a high probability of passing. In the decision tree, the predictive factors for passing result had the greatest influence in the order of graduation status (x²=12366.843, p<0.001) and examination area (x²=312.446, p<0.001). Logistic regression analysis showed a specificity of 39.6% and sensitivity of 95.5%; while decision tree analysis showed a specificity of 45.8% and sensitivity of 94.7%. In classification accuracy, logistic regression and decision tree analysis showed 87.6% and 88.0% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. Additionally, whether actual test takers passed the national physical therapy examination could be determined, by applying the constructed prediction model and prediction rate.

• Physical Therapy Rehabilitation Science
• /
• v.12 no.2
• /
• pp.80-91
• /
• 2023

Objective: The purpose of this study is to identify factors that affect the incidence of hypertension using logistic regression and decision tree analysis, and to build and compare predictive models. Design: Secondary data analysis study Methods: We analyzed 9,859 subjects from the Korean health panel annual 2019 data provided by the Korea Institute for Health and Social Affairs and National Health Insurance Service. Frequency analysis, chi-square test, binary logistic regression, and decision tree analysis were performed on the data. Results: In logistic regression analysis, those who were 60 years of age or older (Odds ratio, OR=68.801, p<0.001), those who were divorced/widowhood/separated (OR=1.377, p<0.001), those who graduated from middle school or younger (OR=1, reference), those who did not walk at all (OR=1, reference), those who were obese (OR=5.109, p<0.001), and those who had poor subjective health status (OR=2.163, p<0.001) were more likely to develop hypertension. In the decision tree, those over 60 years of age, overweight or obese, and those who graduated from middle school or younger had the highest probability of developing hypertension at 83.3%. Logistic regression analysis showed a specificity of 85.3% and sensitivity of 47.9%; while decision tree analysis showed a specificity of 81.9% and sensitivity of 52.9%. In classification accuracy, logistic regression and decision tree analysis showed 73.6% and 72.6% prediction, respectively. Conclusions: Both logistic regression and decision tree analysis were adequate to explain the predictive model. It is thought that both analysis methods can be used as useful data for constructing a predictive model for hypertension.

• Journal of Distribution Science
• /
• v.17 no.10
• /
• pp.61-71
• /
• 2019

Purpose - This study aims to explore the impact of factors on Korean consumer goods purchase decision of Vietnam's Generation Z. Research design, data, and methodology - A mixed research method was utilized in this study including focus group, in-depth interview, pilot study, and official study. The conceptual model and hypothesis were tested using data collected cross-sectional by questionnaire, from a sample of 439 respondents, by both electronic and paper surveys with non-probability and convenience sampling. The SPSS 20 and AMOS 20 software were employed to analyze the data. Results - Results showed that Vietnam's Generation Z was strongly impacted by social media, Hallyu, country of origin, social norms, and perceived usefulness. Besides, Korean consumer goods purchase decision of Vietnam's Generation Z also were impacted by intermediary factors such as trust, social norms, product involvement, perceived quality, perceived usefulness, attitude, and buying intention. There were differences in factors affecting the purchase decision of the boy and girl Generation Z group. Conclusions - The factors impacting on Korean consumer goods decision of Vietnam's Generation Z are very important for Korean firms and government. The findings provide Korean firms opportunity for appropriate to be carried out factors impacting Korean consumer goods to generation Z in Vietnam successful.

• The Journal of Asian Finance, Economics and Business
• /
• v.5 no.3
• /
• pp.81-93
• /
• 2018

This study developed and tested the impact of hallyu 4.0, social media, and consumer ethnocentrism on the decision to purchase Korean products of Generation C in Vietnam. Both qualitative and quantitative methodologies were utilized in this study. Qualitative research was first carried out with in-depth interview, conducted to derive measurement items for the interested constructs. Quantitative research used cross-sectional field design by pilot study and official study. The model was tested and developed using data collected by questionnaires, from a sample of 575 respondents, by both electronic and paper surveys with non-probability and convenience sampling techniques. SPSS 20 and AMOS 20 software were employed to analyze the data. The results of structural equation modeling showed that hallyu 4.0, social media, and consumer ethnocentrism influenced the intermediates variables: subject norms, trust, attitude and behavioral intention and influenced purchase decision. The hallyu 4.0, social media, and consumer ethnocentrism are independent variables. They impact purchase decision through mediating variables such as trust, subjective norms, attitude and behavioral intention. Social media influences not only to trust but also to subjective norms. Subjective norms influence on purchase decision. This study also discovers an interesting fact that trust and attitude variables have an impact on behavioral intention and purchase decision.

• The Journal of the Korea Contents Association
• /
• v.10 no.3
• /
• pp.21-31
• /
• 2010

In the architectural design technologies being changed recently, there is the study to develop the way that will enable the designers to get access logically to the processes of deciding the values of design factors which depend on the experience of the designers. This study, which is one part of those studies, has been carried out to develop the model that can decide the values logically for the design factors having the character that the design values are changed by the time variation out of design factors involved in the building energy saving design. As a result, the structure of the decision model which can decide the design values logically from the computer simulation that solve the problem by interpreting the real world as the probability distribution, has been established through this study. For the application and verification of these decision model, the case study has been carried out for the outdoor climate factors that stand for the design factors having the time variation.

• Journal of Labour Economics
• /
• v.41 no.4
• /
• pp.107-131
• /
• 2018

This paper presents a model for the Minimum Wage Commission's decision process and analyzes the strategic actions of the participants in the process. The Minimum Wage Commission has used two ways of setting the minimum wage. The commission has voted either on the labor's against the management' final proposals or has voted on the public interest commissioners' proposal. According to the model, the minimum wage is determined at a level that is very close to or at a level preferred by the median voter among the public interest commissioners. But the probability of adopting labor or management proposal is ex-ante the same. Empirical evidence from the minimum wage decision process is consistent with the predictions of the model. The probability of adopting the labor's proposal in the minimum wage commission voting is not statistically significantly different from 50%. The model also suggests that the preference of the median voter among public interest commissioners determines the minimum wage level. Since the government appoints public interest commissioners and thus, in fact, the median voters, the government can decide the minimum wage level. This proposition is also consistent with data. The annual growth rate of the minimum wage under the progressive governments is higher than under conservative governments.