• Asian Pacific Journal of Cancer Prevention
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• v.15 no.15
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• pp.6253-6255
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• 2014

Background: Colorectal cancer (CRC) is considered to be a main cause of malignancy-related death in the world, being commonly diagnosed in both men and women. It is the third leading cause of cancer dependent death in the world and there are one million new cases diagnosed per year. In Iran the incidence of colorectal cancer has increased during the last 25 years and it is the fifth cause of cancer in men and the third in women. Materials and Methods: In this article we analyzed the survival of 475 colorectal patients of Taleghani hospital in Tehran with the semi-parametric competing-risks model. Results: There were 55% male cases and at the time of the diagnosis most of the patients were between 48 and 67years old. The probability of a patient death from colorectal cancer with survival of more than 25 years was about 0.4. Body mass index, height, tumour site and gender had no influence. Conclusions: According to these data and by using semi-parametric competing-risks method, we found out that only age at diagnosis has a significant effect on these patient survival time.

• The Korean Journal of Applied Statistics
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• v.20 no.2
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• pp.357-371
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• 2007

For the time-to-failure data with competing risks, cumulative incidence functions (CIFs) are commonly estimated using nonparametric methods. If the cases of events due to the cause of primary interest are infrequent relative to other cause of failure, nonparametric methods may result in rather imprecise estimates for CIF. In such cases, Bryant et al. (2004) suggested to model the cause-specific hazard of primary interest parametrically, while accounting for the other modes of failure using nonparametric estimator. We represented the semiparametric cumulative incidence estimator and extended to the model of Weibull and log-normal distribution. We also conducted simulations to access the performance of the semiparametric cumulative incidence estimators and to investigate the impact of model misspecification in log-normal cause-specific hazard model.

• Journal of Korean Institute of Industrial Engineers
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• v.32 no.2
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• pp.120-125
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• 2006

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.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.947-955
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• 2017

A competing risk analysis has been applied when subjects experience more than one type of end points. Geskus (2011) showed three types of estimators of CIF are equivalent under left truncated and right censored data. We extend his approach to an interval censored competing risk data by using a modified risk set and evaluate their performance under several sample sizes. These estimators show very similar results. We also suggest a test statistic combining Sun's test for interval censored data and Gray's test for right censored data. The test sizes and powers are compared under several cases. As a real data application, the suggested method is applied a data where the feasibility of the vaccine to HIV was assessed in the injecting drug uses.

• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1311-1327
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• 2016

We propose a multi-state model for analyzing semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the 'illness-death model', which composes three states, such as 'healthy', 'diseased', and 'dead'. The state of 'diseased' can be considered as an intermediate event. Two more states are added into the illness-death model to describe missing events caused by a loss of follow-up before the end of the study. One of them is a state of 'LTF', representing a lost-to-follow-up, and the other is an unobservable state that represents the intermediate event experienced after LTF occurred. Given covariates, we employ the Cox proportional hazards model with a normal frailty and construct a full likelihood to estimate transition intensities between states in the multi-state model. Marginalization of the full likelihood is completed using the adaptive Gaussian quadrature, and the optimal solution of the regression parameters is achieved through the iterative Newton-Raphson algorithm. Simulation studies are carried out to investigate the finite-sample performance of the proposed estimation procedure in terms of the empirical coverage probability of the true regression parameter. Our proposed method is also illustrated with the dataset adapted from Helmer et al. (2001).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.40 no.3
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• pp.92-98
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• 2017

The rapid growth of engineering technology and the emergence of systemized and large-scale engineering systems have resulted in complexity and uncertainty throughout the lifecycle activities of engineering systems. This complex and large-scale engineering system consists of numerous components, but system failure can be caused by failure of any one of a number of components. There is a real difficulty in managing such a complex and large-scale system as a part. In order to efficiently manage the system and have high reliability, it is necessary to structure a system with a complex structure as a sub-system. Also, in the case of a system in which cause of failures exist at the same time, it is required to identify the correlation of the components lifetime and utilize it for the design policy or maintenance activities of the system. Competitive risk theory has been used as a theory based on this concept. In this study, we apply the competitive risk theory to the models with combined structure of series and parallel which is the basic structure of most complex engineering systems. We construct a competing risks model and propose a mathematical model of net lifetime and crude lifetime for each cause of failure, assuming that the components consisting a parallel system are mutually dependent. In addition, based on the constructed model, the correlation of cause of failure is mathematically analyzed and the hazard function is derived by dividing into net lifetime and crude lifetime.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.239-244
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• 1998

The environment of the access network service market is characterized by uncertain demand and various competing alternative technologies. In Korea, despite the introduction of competition, dominant Public Network Operator(PNO) still leads the market. Therefore, the decision of PNO has a great impact on the access network evolution. In this paper, we propose an model which aims to reduce risks and both investment and operating costs, to cope with the uncertain demand and technology evolution. We expect this model to provide a tool analyze risks and evaluate various strategies on the network evolution.

• Journal of Labour Economics
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• v.39 no.1
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• pp.1-31
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• 2016

This paper investigates the cyclical behavior of UI benefit take-up rate, the share of unemployed persons who are eligible for job seekers' allowances(JSA) and actually receive them. Using Korea's Employment Insurance DB, it also identifies the factors linked to the decision to take up job seekers' allowances. The results show that the take-up rate is countercyclical and leads both unemployment rate and Coincident Composite Index cyclical component by 6 months and is positively correlated with replacement rate and benefit duration, suggesting that extending benefit duration and raising benefit level can boost benefit claims to increase take-up rates in Korea.

• The Korean Journal of Applied Statistics
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• v.30 no.2
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• pp.233-241
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• 2017

Interval censored data frequently occur in observation studies where the subject is followed periodically. In this paper, our interest is to suggest a test statistic to compare the CIF of two groups with interval censored failure time data in the presence of competing risks. Gray (1988) suggested a test statistic for right censored data that motivated a well-known Fine and Gray's subdistribution hazard model. A multiple imputation technique is adopted to adopt Gray's test statistic to interval censored data. The powers and sizes of the suggested method are investigated through diverse simulation schemes. The main merit of the suggested method is its simplicity to implement with existing software for right censored data. The method is illustrated by analyzing Bangkok's HIV cohort dataset.

• The Korean Journal of Applied Statistics
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• v.30 no.4
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• pp.539-553
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• 2017

We propose a multi-state model to analyze semi-competing risks data with interval-censored or missing intermediate events. This model is an extension of the three states of the illness-death model: healthy, disease, and dead. The 'diseased' state can be considered as the intermediate event. Two more states are added into the illness-death model to incorporate the missing events, which are caused by a loss of follow-up before the end of a study. One of them is a state of the lost-to-follow-up (LTF), and the other is an unobservable state that represents an intermediate event experienced after the occurrence of LTF. Given covariates, we employ the Lin and Ying additive hazards model with log-normal frailty and construct a conditional likelihood to estimate transition intensities between states in the multi-state model. A marginalization of the full likelihood is completed using adaptive importance sampling, and the optimal solution of the regression parameters is achieved through an iterative quasi-Newton algorithm. Simulation studies are performed to investigate the finite-sample performance of the proposed estimation method in terms of empirical coverage probability of true regression parameters. Our proposed method is also illustrated with a dataset adapted from Helmer et al. (2001).