Comparison of Survival Models for Analyzing Prognostic Factors in Gastric Cancer Patients

Document Type : Research Articles

Authors

1 Department of Biostatistics, Faculty of Medicine, Arak University of Medical Sciences, Arak,Iran.

2 Pars Clinicopathology Clinicopathology Laboratory, Arak, Iran.

3 Trauma Research Center, Department of Community Medicine, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.

Abstract

Objective: There are a number of models for determining risk factors for survival of patients with gastric cancer.
This study was conducted to select the model showing the best fit with available data. Methods: Cox regression and
parametric models (Exponential, Weibull, Gompertz, Log normal, Log logistic and Generalized Gamma) were utilized in
unadjusted and adjusted forms to detect factors influencing mortality of patients. Comparisons were made with Akaike
Information Criterion (AIC) by using STATA 13 and R 3.1.3 softwares. Results: The results of this study indicated that
all parametric models outperform the Cox regression model. The Log normal, Log logistic and Generalized Gamma
provided the best performance in terms of AIC values (179.2, 179.4 and 181.1, respectively). On unadjusted analysis,
the results of the Cox regression and parametric models indicated stage, grade, largest diameter of metastatic nest,
largest diameter of LM, number of involved lymph nodes and the largest ratio of metastatic nests to lymph nodes,
to be variables influencing the survival of patients with gastric cancer. On adjusted analysis, according to the best model
(log normal), grade was found as the significant variable. Conclusion: The results suggested that all parametric models
outperform the Cox model. The log normal model provides the best fit and is a good substitute for Cox regression.

Keywords

Main Subjects

Asian Pacific Journal of Cancer Prevention
Volume 19, Issue 3
March 2018
Pages 749-753

Files

History

  • Receive Date: 18 September 2017
  • Revise Date: 03 December 2017
  • Accept Date: 09 January 2018

Share

How to cite

Statistics