Background: It can be used different models such as nonparametric, semi-parametric and parametric to estimation of survival. Depending on the nature of the data, some methods may be more appropriate. Using of appropriate model result more accuracy estimation of survival and it's affected. so the aim of this study estimation of survival and determination of its related risk factor. Materials and Methods: In this study, 169 patients with colorectal cancer who had treated in Hamadan MRI Center from March 2005 to Jun 2013 were evaluated. Survival time of patients is considered from diagnosis time to death or end of study. The effect of Demographic and clinical risk factors on survival is assessed by parametric model. follow up of patient Data were analyzed using Spss16 and R statistical software.

Results: The median survival was 4.3±40 months. Univariate analysis showed that the effect of staging of tumor, tumor grade, histology type of tumor, metastasis and relapse on survival were significant but in multivariate model histology type of tumor and metastasis were significant.

Conclusion: using of appropriate statistical model for detection of affected risk factor on patient with colorectal cancer can be prevented progression of disease and increased of survival of them.

Background: Colorectal cancer is the most common gastrointestinal cancer. Investigating the factors that predict survival time for these patients is important.The purpose of this study was comparison of parametric models by estimating the prediction error and also identifying the effective factors on predicted survival time of patients with colorectal cancer.

Materials and Methods: This cohort study was conducted with 600 patients who were suffered from colorectal cancer in Taleghani Hospital of Tehran between 2001 to 2005 and they were followed up for at least 5 years. For identifying the effective factors on survival time, of the patients we analyzed the data by some parametric models such as Weibull, Exponential and Log logistic and compared these models with the estimation of prediction error by apparent loss method.

Results: Among 600 patients there was 344 men (57.3%) and 256 wemon (47.7%). Of total, 151 patients were died that 62.3% of them were men. Univariate analysis showed that the effect of BMI, sex, staging of tumor, tumor site were significant but in multivariate model staging of tumor and BMI were significant. By the estimation of prediction error, the best model was Log logistic.

Conclusion: With respect to the importance of survival time prediction, we found that we can use the prediction error to compare the parametric models. In addition, because of effectiveness of tumor stages and BMI in the patients’ survival time, survival time could be increased by an on-time diagnosis and an appropriate controled diet.