Parametric Model Evaluation in Examining the Survival of Gastric Cancer Patients Andits Influencing Factors

Abolfazl Nikpour,Alireza Khalilian,Hosien Ranjbaran,Jamshid Yazdani-Charati ,Iradj Maleki

doi:10.5539/gjhs.v9n3p260

Abstract

BACKGROUND: Cox proportional hazard model is the most common technique to analysis the variables effect on survival time, but under certain circumstances, parametric models may offer advantages over Cox’s model. In this study we use cox regression and alternative parametric models such as Weibull, exponential, log-normal, logistics and gamma model to evaluate factors affecting survival of patients with gastric cancer. Comparisons were made to find the best model.METHOD: In this study, data from 643 patients with gastric cancer who were referred to Imam Khomeini hospital with personal details during 2007 to 2013 have been reviewed in order to determine the survival rate of gastric cancer. It was observed that 74 cases were eliminated due to incomplete information and 569 persons were examined. Akaike Information model was used for comparison between models.RESULT: Of a total of 569 patients, 329 (57.8%) died during the period. The figure of Cox-Snell residuals indicates that only the exponential model does not have better fitness. Weibull, log-normal, log-logistic and gamma models show the better fitness because points are on straight line. At the time of diagnosis, stage with (p&lt;0.0008) and metastasis with (p&lt;0.0219) were subjected to higher risk of death.CONCLUSION: Based on Akaike's criterion, the Weibull model with Akaike value of 257.165 is the most favorable for survival data.

Highlights

Analysis of survival data is a set of methods that is applied for analyzing data at which response variable is the time of occurrence a special event
Cox proportional hazard model is the most common technique to analysis the variables effect on survival time, but under certain circumstances, parametric models may offer advantages over Cox’s model
This study aims at investigation of parametric models in the evaluation of patients survival with cancer and its’ affecting factors

Summary

Introduction

Analysis of survival data is a set of methods that is applied for analyzing data at which response variable is the time of occurrence a special event. The features of time data are positivity and skewed to the right Another characteristic of survival time is existence of incomplete (unfinished or censored) data. The study of survival data is of longitudinal type and person has been followed up until the occurrence of an event or being censored (Kleinbaum & Klein, 2012; Ravangard et al, 2011). There are three methods for analysis of survival data: Nonparametric method (Kaplan-Meier, Nelson Allen and life table), semi-parametric method (Cox regression), and Parametric method (exponential, Weibull, gamma, lognormal, etc.). Cox proportional hazard model is the most common technique to analysis the variables effect on survival time, but under certain circumstances, parametric models may offer advantages over Cox’s model.

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