Prognostic factors for the survival of patients with esophageal cancer in Northern Iran

Abstract

Background: Esophageal cancer is the 8(th) most common cancer and the 6(th) leading cause of cancer-related death, worldwide. In Iran, the high incidence rates of this type of cancer have been reported from the Caspian Sea region. This study aimed at assessing the factors affecting survival of patients with esophageal cancer in neighbor provinces around Caspian Sea using parametric and semi-parametric models with univariate gamma frailty model.

Methods: In this study, we performed a prospective review of 359 patients presenting with esophageal cancer from 1990 to 1991. The data were obtained using the Cancer Registry information existed in Babol research center in Iran. Study participants were followed-up until 2006 for a period of 15 years. Hazard ratio was used to interpret the risk of death. The Akaike Information Criterion (AIC) was considered as a criterion to select the best model(s).

Results: Of the 359 patients, 225 (62.7%) were male with a mean age of 60.0 years and 134 (37.3%) were female with a mean age of 55.3 at the time of diagnosis. 1- , 3- and 5-year survival rates after diagnosis were 23%, 15% and 13% , respectively. Comparison between Cox and parametric models of AIC showed that the overall fitting was improved under parametric models. Among parametric models, the log-logistic model with gamma frailty provided better performance than other models. Using this model, we found that gender (p=0.012) and family history of cancer (p= 0.003) were significant predictors.

Conclusions: Since the proportionality assumption of the Cox model was not held (p = 0.01), the Cox regression model was not an appropriate choice for analyzing our data. According to our findings, log logistic model with gamma frailty could be considered as a useful statistical model in survival analysis of patients with esophageal cancer rather than Cox and log-normal models.

Keywords: Esophageal Cancer; Parametric Model; Survival Analysis; Univariate Gamma Frailty Model.

Figure 1

Comparison of survival functions of patients with esophageal cancer in different age groups

Figure 2

Cox-Snell residuals obtained from fitting various survival models to the esophageal cancer data. The panels indicate the Cox-Snell residuals (together with their cumulative hazard function) obtained from fitting different parametric AFT models to the same data via maximum likelihood estimation. Obviously, the lines related to the Cox-Snell residuals of the log-normal and log-logistic models with gamma frailty are nearest to the line through the origin, indicating that these models fit the data best. In addition, the Cox model does not appear to fit the data well; it means that the proportional hazards assumption is violated. These results are consistent with our finding based on Akaike's Information Criterion