Related Experiment Video
Updated: Aug 17, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
An estimator of the survival function based on the semi-Markov model under dependent censorship
Seung-Yeoun Lee1, Wei-Yann Tsai
1Department of Applied Mathematics, Sejong University, 98 Kunja-dong, Kwangjin-gu, Seoul, 143-747, Korea. leesy@sejong.ac.kr
Abstract:
Lee and Wolfe (Biometrics vol. 54 pp. 1176-1178, 1998) proposed the two-stage sampling design for testing the assumption of independent censoring, which involves further follow-up of a subset of lost-to-follow-up censored subjects. They also proposed an adjusted estimator for the survivor function for a proportional hazards model under the dependent censoring model. In this paper, a new estimator for the survivor function is proposed for the semi-Markov model under the dependent censorship on the basis of the two-stage sampling data. The consistency and the asymptotic distribution of the proposed estimator are derived. The estimation procedure is illustrated with an example of lung cancer clinical trial and simulation results are reported of the mean squared errors of estimators under a proportional hazards and two different nonproportional hazards models.
Related Concept Videos
Censoring Survival Data
Kaplan-Meier Approach
Assumptions of Survival Analysis
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time until a...
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
Survival Tree
Building a Survival Tree
Constructing a survival tree begins...

