Related Experiment Video
Updated: Jun 5, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
[Survival analysis on advanced non-small cell lung cancer with a Buckley-James model]
Tian Xu1, Hong-yu Zhao, Yu Yan
1Epidemiology and Health Statistics Department of Public Health College of Nantong University, Nantong 226001, China.
Objective:
To analyze the risk factors related to survival time of advanced non-small cell lung cancer (NSCLC) and to establish a prediction model on survival time.
Methods:
From 2004 - 2006, 184 patients with advanced NSCLC were enrolled in the Affiliated Hospital to the Nantong Medical College. Related risk factors were analyzed, using the Buckley-James model. Both actual and predicted survival time were compared by log-rank test.
Results:
Through Buckley-James model analysis, data showed that KPS, clinical stage, treatment and pre-treatment hemoglobin were main influencing factors on survival time. Regression equation appeared to be lnMONTH=0.0108 KPS+0.0238 HB+0.4614 IIIb+0.8027 IIIa+0.3869 (radiotherapy+chemotherapy)+0.507 (radiotherapy + operation) + 0.6082 (chemotherapy + operation) - 2.098. There was no statistical difference between the prediction and the actual models of survival time by log-rank test (P=0.575>0.05).
Conclusion:
KPS, clinical stage, treatment and pre-treatment hemoglobin might be associated. Both the prognosis of patients with advanced NSCLC and the prediction model seemed to have practical significances.
Related Concept Videos
Cancer Survival Analysis
Comparing the Survival Analysis of Two or More Groups
Assumptions of Survival Analysis
Introduction To Survival Analysis
The primary goal of survival analysis is to estimate survival time—the time until a...
Kaplan-Meier Approach
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...