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So Young Sohn - One of the best experts on this subject based on the ideXlab platform.
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Competing Risk Model for predicting stabilization period of university spin off ventures
International Entrepreneurship and Management Journal, 2017Co-Authors: Joon Hyung Cho, So Young SohnAbstract:University spin-offs (USOs) are considered as a means of value creation derived from academic research. However, due to financial and managerial constraints, many USOs find it difficult to maintain their business and achieve stabilization. In this study, we investigate the factors affecting the time taken by USOs to reach their break-even point or to secure initial investment from venture capitals, whichever comes earlier. We specifically examine the effect of knowledge diversity, experiential knowledge, intellectual property, and the communication capacity of the USO founders on the time taken to reach stabilization. A Competing Risk Model is applied to the survey data of USO founders in Korea. According to our findings, knowledge diversity, experiential knowledge, and communication capacity are significantly related to the time taken to reach the break-even point. Knowledge diversity, intellectual property, and communication capacity, on the other hand, are associated with the time taken to receive initial investment from venture capitals. Our study can contribute toward deriving strategies for USOs to increase their stabilization speed.
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Competing Risk Model for technology credit fund for small and medium sized enterprises
Journal of Small Business Management, 2010Co-Authors: So Young Sohn, Hyejin JeonAbstract:Despite the need to foster a technology-intensive industry, most Korean SMEs (small and medium-sized enterprises) are faced with the difficulty of raising funds. To resolve this problem, the government set up the technology credit fund to give loans to enterprises that achieve a certain technology evaluation score. However, many of the recipient SMEs fail to pay back the loans for various reasons. In this paper, we distinguish two causes of default due to owner and company, respectively, using the Competing Risk Model. The proposed prediction Models for Competing defaults are expected to contribute to the healthy management of technology finance.
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Competing Risk Model for Technology Credit Fund for Small and Medium‐Sized Enterprises
Journal of Small Business Management, 2010Co-Authors: So Young Sohn, Hyejin JeonAbstract:Despite the need to foster a technology-intensive industry, most Korean SMEs (small and medium-sized enterprises) are faced with the difficulty of raising funds. To resolve this problem, the government set up the technology credit fund to give loans to enterprises that achieve a certain technology evaluation score. However, many of the recipient SMEs fail to pay back the loans for various reasons. In this paper, we distinguish two causes of default due to owner and company, respectively, using the Competing Risk Model. The proposed prediction Models for Competing defaults are expected to contribute to the healthy management of technology finance.
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Competing Risk Model for mobile phone service
Technological Forecasting and Social Change, 2008Co-Authors: So Young Sohn, Jae Kang LeeAbstract:Abstract Since the Korean government implemented the “Number Portability System” in the domestic mobile communications market, mobile communication companies have been striving to hold onto existing customers and at the same time to attract new customers. This paper presents a Competing Risk Model that considers the characteristics of a customer in order to predict the customer's mean residual life under the “Number Portability System.” Competing causes for churning considered are pricing policy, quality of communication, and usefulness of service. It has been observed that the customers who pay more are less sensitive to pricing policy; younger people are less sensitive than older people to the change of the quality of communication; and women are more sensitive than men in terms of usefulness of service. We expect that the result of this study can be used as a guideline for effective management of mobile phone customers under the number portability system.
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Competing Risk Model for Mobile Phone Service
2006Co-Authors: Jae Kang Lee, So Young SohnAbstract:Since Korean government has implemented the "Number Portability System" in the domestic mobile communications market, mobile communication companies have been striving to hold onto existing customers and at the same time to attract new customers. This paper presents a Competing Risk Model that considers the characteristics of a customer in order to predict the customer's life under the "Number Portability System." Three Competing Risks considered are pricing policy, quality of communication, and usefulness of service. It was observed that the customers who pay more are less sensitive on pricing policy younger people are less sensitive than older people to the quality of communication and women are more sensitive than men to the degree of usefulness of service. We expect that the result of this study can be used as a guideline for effective management of mobile phone customers under the Number Portability System.
Hyejin Jeon - One of the best experts on this subject based on the ideXlab platform.
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Competing Risk Model for technology credit fund for small and medium sized enterprises
Journal of Small Business Management, 2010Co-Authors: So Young Sohn, Hyejin JeonAbstract:Despite the need to foster a technology-intensive industry, most Korean SMEs (small and medium-sized enterprises) are faced with the difficulty of raising funds. To resolve this problem, the government set up the technology credit fund to give loans to enterprises that achieve a certain technology evaluation score. However, many of the recipient SMEs fail to pay back the loans for various reasons. In this paper, we distinguish two causes of default due to owner and company, respectively, using the Competing Risk Model. The proposed prediction Models for Competing defaults are expected to contribute to the healthy management of technology finance.
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Competing Risk Model for Technology Credit Fund for Small and Medium‐Sized Enterprises
Journal of Small Business Management, 2010Co-Authors: So Young Sohn, Hyejin JeonAbstract:Despite the need to foster a technology-intensive industry, most Korean SMEs (small and medium-sized enterprises) are faced with the difficulty of raising funds. To resolve this problem, the government set up the technology credit fund to give loans to enterprises that achieve a certain technology evaluation score. However, many of the recipient SMEs fail to pay back the loans for various reasons. In this paper, we distinguish two causes of default due to owner and company, respectively, using the Competing Risk Model. The proposed prediction Models for Competing defaults are expected to contribute to the healthy management of technology finance.
Bertram L Kasiske - One of the best experts on this subject based on the ideXlab platform.
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beyond median waiting time development and validation of a Competing Risk Model to predict outcomes on the kidney transplant waiting list
Transplantation, 2016Co-Authors: Allyson Hart, Nicholas Salkowski, Jon J Snyder, Ajay K Israni, Bertram L KasiskeAbstract:BackgroundMedian historical time to kidney transplant is misleading because it does not convey the Competing Risks of death or removal from the waiting list. We developed and validated a Competing Risk Model to calculate likelihood of outcomes for kidney transplant candidates and demonstrate how thi
Junji Tagami - One of the best experts on this subject based on the ideXlab platform.
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influence of central and peripheral dentin on micro tensile bond strength estimated using a Competing Risk Model
Journal of The Mechanical Behavior of Biomedical Materials, 2021Co-Authors: Antonin Tichy, Marek Brabec, Pavel Bradna, Keiichi Hosaka, Ayaka Chiba, Junji TagamiAbstract:Abstract The bonding performance of dental adhesives is most frequently evaluated using the micro-tensile bond strength (μTBS) test. Despite lacking evidence, peripheral specimens are often discarded to avoid regional variability. This study, therefore, examined whether μTBS to central and peripheral dentin differed. Dentin surfaces of extracted human molars were bonded with various self-etch adhesives, built up with a resin composite, cut into beams, and stressed in tension. Failure mode was classified as adhesive, cohesive in dentin, or other using scanning electron microscopy. Since cohesive failures in dentin were frequent and could confound μTBS results, the data from central/peripheral dentin were analyzed using a Weibull Competing Risk (CR) Model distinguishing failure modes, and its outcomes were compared to a conventional failure mode non-distinguishing Weibull Model. Based on the strength data of cohesively failed specimens, the CR Model also estimated the strength of dentin. For comparison, the ultimate tensile strength (UTS) of dentin was measured in both regions. The conventional Model suggested that peripheral μTBS was higher than central μTBS. Conversely, the CR Model disclosed no significant difference in μTBS between the regions but indicated a higher strength of peripheral dentin. This finding was confirmed by UTS measurements, and further supported by the significantly higher incidence of cohesive failures in central dentin. Therefore, peripheral specimens can be used in the μTBS test as well as central ones, but a CR Model should be used for statistical analysis if cohesive failures in dentin are frequent, as the strength of peripheral dentin is higher.
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a Competing Risk Model for bond strength data analysis
Dental Materials, 2020Co-Authors: Antonin Tichy, Marek Brabec, Pavel Bradna, Keiichi Hosaka, Junji TagamiAbstract:Abstract Objectives A Competing Risk (CR) Model distinguishing adhesive, cohesive and mixed failures as Competing events was used for the analysis of micro-tensile bond strength (μTBS) data and compared with a conventional failure mode non-distinguishing survival Model. Methods Fifty human molars were bonded using five universal adhesives (n = 10) and subdivided according to aging conditions (24-h water storage, thermocycling). After μTBS to dentin was tested, a fractographic analysis was performed using scanning electron microscopy. Survival analyses of the μTBS data were performed using both a failure mode distinguishing Weibull CR Model, and a conventional failure mode non-distinguishing Weibull Model. Weibull shape (m) and scale (σθ) parameters were calculated for both Models using the maximum likelihood estimation method, and strength at 10 % probability of failure, σ0.10, was estimated. Groups were compared using 95 % confidence intervals. Results CR-Model estimates of σθ and σ0.10 for adhesive failures were higher than those of the conventional Model, more markedly in groups with lower percentages of adhesive failures. CR-Model strength estimates for cohesive failures were similar in all groups regardless of their bond strengths and failure mode distributions. Significance Merging all bond-strength data into one dataset irrespective of the failure mode may result in a severe underestimation of bond strength, especially in groups with low incidence of adhesive failures. Bond-strength data analysis using a CR Model could provide more accurate estimates of bond strength, and strength estimates for cohesive failures which were apparently independent of bond strength could serve as an internal validity indicator of the CR Model.
Soren M Bentzen - One of the best experts on this subject based on the ideXlab platform.
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a Competing Risk Model of first failure site after definitive chemoradiation therapy for locally advanced non small cell lung cancer
Journal of Thoracic Oncology, 2018Co-Authors: Lotte Nygard, Ivan R Vogelius, Barbara M Fischer, Andreas Kjaer, Seppo W Langer, M C Aznar, G Persson, Soren M BentzenAbstract:Abstract Introduction The aim of the study was to build a Model of first failure site– and lesion-specific failure probability after definitive chemoradiotherapy for inoperable NSCLC. Methods We retrospectively analyzed 251 patients receiving definitive chemoradiotherapy for NSCLC at a single institution between 2009 and 2015. All patients were scanned by fludeoxyglucose positron emission tomography/computed tomography for radiotherapy planning. Clinical patient data and fludeoxyglucose positron emission tomography standardized uptake values from primary tumor and nodal lesions were analyzed by using multivariate cause-specific Cox regression. In patients experiencing locoregional failure, multivariable logistic regression was applied to assess Risk of each lesion being the first site of failure. The two Models were used in combination to predict probability of lesion failure accounting for Competing events. Results Adenocarcinoma had a lower hazard ratio (HR) of locoregional failure than squamous cell carcinoma (HR = 0.45, 95% confidence interval [CI]: 0.26–0.76, p = 0.003). Distant failures were more common in the adenocarcinoma group (HR = 2.21, 95% CI: 1.41–3.48, p p p Conclusions We developed a failure site–specific Competing Risk Model based on patient- and lesion-level characteristics. Failure patterns differed between adenocarcinoma and squamous cell carcinoma, illustrating the limitation of aggregating them into NSCLC. Failure site–specific Models add complementary information to conventional prognostic Models.