Alternative for the Cox Regression model: using Parametric Models to Analyze the Survival of Cancer Patients

AUTHORS

Mohamad Amin Pourhoseingholi 1 , * , A Pourhoseingholi 1 , M Vahedi 1 , B Moghimi Dehkordi 1 , A Safaee 1 , S Ashtari 1 , MR Zali 1

1 Research Center for Gastroenterology and Liver Disease, Shahid Beheshti University of Medical Sciences, Tehran, Iran

How to Cite: Pourhoseingholi M A, Pourhoseingholi A, Vahedi M, Moghimi Dehkordi B, Safaee A, et al. Alternative for the Cox Regression model: using Parametric Models to Analyze the Survival of Cancer Patients, Int J Cancer Manag. 2011 ; 4(1):e80720.

ARTICLE INFORMATION

International Journal of Cancer Management: 4 (1); e80720
Published Online: March 30, 2011
Article Type: Research Article
Received: October 14, 2010
Accepted: December 04, 2010

Crossmark

CHEKING

READ FULL TEXT
Abstract

Background: Although the Cox proportional hazard regression is the most popular model for analyzing the prognostic factors on survival of cancer patients, under certain circumstances, parametric models estimate the parameter more efficiently than the Cox model. The aim of this study was to compare the Cox regression model with parametric models in patients with gastric cancer who registered at Taleghani hospital, Tehran, Iran.

Methods: In a retrospective cohort study, 746 patients with gastric cancer were studied from February 2003 through January 2007. Gender, age at diagnosis, distant metastasis, extent of wall penetration, tumor size, histology type, tumor grade, lymph node metastasis and pathologic stage were selected as prognosis , and entered to the models. Lognormal, Exponential, Gompertz, Weibull, Loglogistic and Gamma regression were performed as parametric models ,and Akaike Information Criterion (AIC) were used to compare the efficiency of the models.

Results: Based on AIC, Log logistic is an efficient model. Log logistic analysis indicated that wall penetration and presence of pathologic distant metastasis were potential risks for death in full and final model analyses.

Conclusion: In the multivariate analysis, all the parametric models fit better than Cox with respect to AIC; and the log logistic regression was the best model among them. Therefore, when the proportional hazard assumption does not hold, these models could be used as an alternative and could lead to acceptable conclusions.

Keywords

Cox Parametric model Gastric cancer Survival analysis

© 2011, Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

Fulltext

The Full text is available in PDF.

COMMENTS

LEAVE A COMMENT HERE: