• 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.

• Communications for Statistical Applications and Methods
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• v.23 no.6
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• pp.555-562
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• 2016

Interval censored data often occur in an observational study where the subject is followed periodically. Instead of observing an exact failure time, two inspection times that include it are available. There are several methods to analyze interval censored failure time data (Sun, 2006). However, in the presence of competing risks, few methods have been suggested to estimate covariate effect on interval censored competing risk data. A sub-distribution hazard model is a commonly used regression model because it has one-to-one correspondence with a cumulative incidence function. Alternatively, Klein and Andersen (2005) proposed a pseudo-value approach that directly uses the cumulative incidence function. In this paper, we consider an extension of the pseudo-value approach into the interval censored data to estimate regression coefficients. The pseudo-values generated from the estimated cumulative incidence function then become response variables in a generalized estimating equation. Simulation studies show that the suggested method performs well in several situations and an HIV-AIDS cohort study is analyzed as a real data example.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1271-1281
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• 2015

Competing risk analysis is widely applied to analyze a failure time with more than two causes. This paper discusses the application of a competing risk model to a economic activity state of workers with occupational injuries. In particular, main interest is to estimate the distribution of restarting time two kinds of economic activities, (i) returning to original working place and (ii) finding a new job. In this paper, we applied a cumulative incidence function to evaluate their patterns under several individual factors and working place's factor. Furthermore, a subdistributional regression model is applied to estimate the effect of these factors on the returning time. According to result, worker with higher education, younger age and longer working period had a higher chance to return an original working place while one with more severe injuries and skilled laborer had longer returning time to an original working place.

• ETRI Journal
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• v.27 no.6
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• pp.747-758
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• 2005

The problem of selection among competing models has been a fundamental issue in statistical data analysis. Good fits to data can be misleading since they can result from properties of the model that have nothing to do with it being a close approximation to the source distribution of interest (for example, overfitting). In this study we focus on the preference among models from a family of polynomial regressors. Three decades of research has spawned a number of plausible techniques for the selection of models, namely, Akaike's Finite Prediction Error (FPE) and Information Criterion (AIC), Schwartz's criterion (SCH), Generalized Cross Validation (GCV), Wallace's Minimum Message Length (MML), Minimum Description Length (MDL), and Vapnik's Structural Risk Minimization (SRM). The fundamental similarity between all these principles is their attempt to define an appropriate balance between the complexity of models and their ability to explain the data. This paper presents an empirical study of the above principles in the context of model selection, where the models under consideration are univariate polynomials. The paper includes a detailed empirical evaluation of the model selection methods on six target functions, with varying sample sizes and added Gaussian noise. The results from the study appear to provide strong evidence in support of the MML- and SRM- based methods over the other standard approaches (FPE, AIC, SCH and GCV).

• 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.