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

Abstract

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