• The Korean Journal of Applied Statistics
• /
• v.34 no.6
• /
• pp.957-968
• /
• 2021

Inverse censoring probability weighting (ICPW) is a popular technique in survival data analysis. In applications of the ICPW technique such as the censored regression, it is crucial to accurately estimate the censoring probability. A simulation study is undertaken in this article to see how censoring probability estimate influences model performance in censored regression using the ICPW scheme. We compare three censoring probability estimators, including Kaplan-Meier (KM) estimator, Cox proportional hazard model estimator, and local KM estimator. For the local KM estimator, we propose to reduce the predictor dimension to avoid the curse of dimensionality and consider two popular dimension reduction tools: principal component analysis and sliced inverse regression. Finally, we found that the Cox proportional hazard model estimator shows the best performance as a censoring probability estimator in both mean and median censored regressions.

• 한국데이터정보과학회:학술대회논문집
• /
• 2003.05a
• /
• pp.109-119
• /
• 2003

In this paper we propose a local smoothing of the Nelson type estimator for the survival function based on an approximation by the Weibull distribution function. It appears that Mean Square Error and Bias of the smoothed estimator of the Nelson type survival function estimator is significantly smaller then that of the smoothed estimator of the Kaplan-Meier survival function estimator.

• Journal of the Korean Data and Information Science Society
• /
• v.14 no.2
• /
• pp.279-287
• /
• 2003

In this paper we propose a local smoothing of the Nelson type estimator for the survival function based on an approximation by the Weibull distribution function. It appears that Mean Square Error and Bias of the smoothed estimator of the Nelson type survival function estimators are significantly smaller than that of the smoothed estimator of the Kaplan-Meier survival function estimator.

• Research in Community and Public Health Nursing
• /
• v.32 no.1
• /
• pp.107-115
• /
• 2021

Purpose: The purpose of this study is to identify factors associated with smoking relapse within six months after quit attempts among workers in small and medium-sized enterprises in South Korea. Methods: The analysis was conducted for a total of 194 people who attempted to quit smoking by applying for a smoking cessation support service at the Regional Tobacco Control Center. The data used in the study were extracted from the Smoking Cessation Service Integrated Information System. Kaplan-Meier estimator and Cox proportional hazards regression model were used to identify variables associated with smoking relapse within six months' time period. Results: Smoking relapse rate within six months was 66.0%, and variables associated with relapse included the cases such as carbon monoxide (CO) at the time of registration (HR: 2.15, 95% CI: 1.10~4.22 for CO ≥20 ppm or more vs.CO <10 ppm), the average number of cigarettes smoked per day (HR: 1.04, 95% CI: 1.00~1.07), and the number of counseling(HR: 0.60, 95% CI: 0.54~0.67). Conclusion: Smoking characteristics and counseling showed one of the strongest correlations with relapse within six months. This implies that it is necessary to understand the smoking characteristics and patterns of workers and to provide continuous smoking cessation counseling tailored to individual characteristics for effective smoking relapse prevention.