• 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.

• Proceedings of the Korean Statistical Society Conference
• /
• 2005.05a
• /
• pp.251-255
• /
• 2005

중도절단된 자료와 표본수가 적은 자료를 가지는 생존분석에서 생존율을 추정하거나 두 집단의 생존율을 비교할 때 정규분포 근사를 가정한 신뢰구간을 이용하는 데는 많은 어려움이 생긴다. 생존함수의 신뢰구간에 대한 중도절단을, 표본의 크기에 따른 다양한 상황의 모의실험을 통하여 Kaplan-Meier, Nelson, 적률 추정량 그리고 cox model의 ${\beta}$을 가지고 붓스트랩을 이용한 신뢰구간과 비모수 신뢰구간, 우도비 신뢰구간의 실제 포함 확률을 비교해보고자 한다.

• The Korean Journal of Applied Statistics
• /
• v.11 no.1
• /
• pp.151-161
• /
• 1998

We study the bootstrap interval estimation for survival function in the Koziol-Green model. We construct the approximate bootstrap confidence intervals for survival function and prove the strong consistency for the bootstrap estimator of survival function. Finally we show that the approximate bootstrap confidence intervals are better in terms of coverage probability than confidence intervals based on asymptotic normal distribution and transformations of survival function via Monte Carlo simulation study.

• The Korean Journal of Applied Statistics
• /
• v.27 no.1
• /
• pp.1-11
• /
• 2014

The Weighted Logrank test and its special case, Logrank test are widely used to compare survival distributions; however, these methods are inappropriate when the sample size is small or censoring distributions are not equal since they use test statistics from approximate distributions. A permutation test can be an alternative for small sample cases; however, this should be used only when censoring distributions are equal. To handle cases with small sample size and unequal censoring distributions, the permutation-imputation method was developed to compare two survival distributions. In this paper, approximate method, permutation method and permutation-imputation method were compared using a Logrank test and Prentice-Wilcoxon test for three or more survival distributions comparison.