• The Korean Journal of Applied Statistics
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• v.29 no.7
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• pp.1231-1246
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• 2016

Cause-specific hazard model (Prentice et al., 1978) and subdistribution hazard model (Fine and Gray, 1999) are mostly used for the right censored survival data with competing risks. Some other models for survival data with competing risks have been subsequently introduced; however, those models have not been popularly used because the models cannot provide reliable statistical estimation methods or those are overly difficult to compute. We introduce simple and reliable competing risk regression models which have been recently proposed as well as compare their methodologies. We show how to use SAS and R for the data with competing risks. In addition, we analyze survival data with two competing risks using five different models.

• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1209-1216
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• 2015

Competing-risks events are often observed in a clustered clinical study such as a multi-center clinical trial. We propose a joint modelling approach via a shared frailty term for competing risks survival data from a cluster. For the inference we use the hierarchical likelihood (or h-likelihood), which avoids an intractable integration. We derive the corresponding h-likelihood procedure. The proposed method is illustrated via the analysis of a practical data set.

• 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.

• 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).

• 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).

• The Journal of the Korea Contents Association
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• v.18 no.10
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• pp.145-157
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• 2018

In this study, we analyzed the effect of the income level of cancer, stroke, and myocardial infarction on mortality by using National Health Insurance Service(NHIS) Cohort 2.0 DB. Patients who newly developed the disease in 2007 were observed till 2015. The analysis used the Cox probability proportional risk model and the competing risk model. The income level used information at the time of the onset of the disease in 2007, categorized into low / mid / high. The results showed that there were differences in the risks of death and secondary disease in patients with cancer, stroke, or myocardial infarction according to the income level. In addition to the need for a social safety net to lower the incidence of early deaths in low-income families, it seems necessary to continue to strengthen universal protection for serious diseases similar to the current policy.

• 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 Journal of Information Systems
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• v.19 no.3
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• pp.127-147
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• 2010

전자상거래의 급속한 발전 속에서 기업들은 세계적 시장에서 경쟁력을 지니기 위해 전자상거래를 이용하여 상품판매, 서비스 제공, 고객과 소통하고자 한다. 그러나, 기존의 연구들은 TAM 모형을 통한 상호관계나 선행관계에만 초점을 맞추고 있기 때문에 좀 더 확장한 모형이 필요하다. 본 연구에서는 상품정보와 서비스 제공수준은 소비자 행동에 영향을 미치는 중요한 요인이지만 연구가 충분치 못한 실정이다. 따라서 본 연구의 목적은 첫째, 상품정보제공수준과 서비스제공수준에 따른 소비자 태도 차이를 조망하고자 한다. 둘째, 기존에 연구가 미약하였던 상품정보제공수준 및 서비스제공수준과 기존에 인지된 가치, 인지된 위험, 구매의도와 구전의도간의 상호인과관계를 조망하고자 한다. 본 연구의 결과는 다음과 같다. 첫째, 상품정보제공수준과 서비스제공수준에 따라 소비자 태도 차이에는 차이가 있었다. 둘째, 상품정보제공수준과 서비스제공수준에 따라 소비자들이 인지된 가치, 인지된 위험, 구매의도, 구전효과에는 차이가 있었다. 셋째, 변수들간에 상호인과관계와 선행관계가 존재하였다. 상품정보제공수준과 서비스 제공수준은 인지된 가치와 인지된 위험에 각각 직접적인 영향을 미쳤다. 뿐만 아니라, 인지된 가치는 인지된 위험, 구매의도, 구전효과에, 인지된 위험은 구매의도와 구전효과에 직접적인 영향을 나타내었다.

• Journal of Labour Economics
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• v.43 no.3
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• pp.1-31
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• 2020

This study analyzed the determinants of self-employment exits among the middle-aged and senior adults. For the analysis, we used KLoSA(Korean Longitudinal Study of Ageing) data from the first(2006) to the sixth(2016) and vocational data, which is a retrospective data surveyed in 2007. Among the reasons for exiting the self-employment, we find that the group that went out of their businesses due to management difficulties were more likely to have economic difficulties after the exit. Therefore, we analyzed the determinants of self-employment exits considering the exit reason due to management difficulties. The analysis model used a competing risk regression model that defined the only exit due to management difficulties as failures. As a result, the significance of gender, age, and education variables, which were well known as determinants of exiting the self-employment, disappeared. On the other hand, we find that the prior experience in the same industry tended to lower the risk of exiting the self-employment. To summarize the results, we suggest that we need some ways to help the middle-aged and senior adults who start their own businesses without any experience in the same industry to prevent them from failures.

• The Korean Journal of Applied Statistics
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• v.37 no.2
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• pp.225-237
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• 2024

In the analysis of competing risks data, some of covariates may not be fully observed for some subjects. In such cases, excluding subjects with missing covariate values from the analysis may result in biased estimates and loss of efficiency. In this paper, we studied multiple imputation and the augmented inverse probability weighting method for regression parameter estimation in the cause-specific proportional hazards model with missing covariates. The performance of estimators obtained from multiple imputation and the augmented inverse probability weighting method is evaluated by simulation studies, which show that those methods perform well. Multiple imputation and the augmented inverse probability weighting method were applied to investigate significant risk factors for the risk of death from breast cancer and from other causes for breast cancer data with missing values for tumor size obtained from the Prostate, Lung, Colorectal, and Ovarian Cancer Screen Trial Study. Under the cause-specific proportional hazards model, the methods show that race, marital status, stage, grade, and tumor size are significant risk factors for breast cancer mortality, and stage has the greatest effect on increasing the risk of breast cancer death. Age at diagnosis and tumor size have significant effects on increasing the risk of other-cause death.