Ma Pourhoseingholi, E Hajizadeh, A Abadi, A Safaee, B Moghimi Dehkordi, Mr Zali,
Volume 3, Issue 1 (9-2007)
Abstract
Background & Objectives: Although Cox regression is commonly used to detect relationships between patient survival and demographic/clinical variables, there are situations where parametric models can yield more accurate results. The objective of this study was to compare two survival regression methods, namely Cox regression and parametric models, in patients with gastric carcinoma registered at Taleghani Hospital, Tehran.
Methods: Using data from 746 patients who had received care at Taleghani Hospital from February 2003 through January 2007, we compared survival rates between different patient groups with both parametric methods and Cox regression models. The former group included Weibull, exponential and log-normal regression we used the Akaike Information Criterion (AIC) and standardized parameter estimates to compare the efficiency of various models. All the analyses were performed with the SAS software and the level of significance was set at P< 0.05.
Results: The results showed a significantly higher chance of survival in the following subgroups: those with age at diagnosis < 35 years, lower tumor size and those without metastases (P< 0.05). According to AIC, Cox and exponentials model are similar in multivariate analysis but in univariate analysis parametric models are more efficient than Cox, except in the case of tumor size. Log-normal appears to be the best model.
Conclusions: Cox and exponential models have similar performance in multivariate analysis. However, it seems that there is no single model that performs substantially better than others in univariate analysis. The data strongly supported the log-normal regression among parametric models it can give more precise results and can be used as an alternative for Cox in survival analysis of patients with gastric cancer.
Mr Ghadimi, M Mahmoodi, K Mohammad, H Zeraati, M Hosseini, A Fotouhi,
Volume 7, Issue 2 (9-2011)
Abstract
Background and Objectives: Each year almost 400,000 people are diagnosed with oesophageal cancer worldwide. Wide variation in incidence has been reported both between countries and in different ethnic groups and populations within a country. The area with the highest reported incidence for oesophageal cancer is the so-called Asian ‘oesophageal cancer belt’, which stretches from eastern Turkey through north-eastern Iran, northern Afghanistan and southern Russia to northern China. In the high risk area of Gonbad in Iran, world age-standardised rates are more than 200 per 100,000 and the male/female ratio is reported as 0.8:1.0.This study aimed to assess the risk factors and demographic factors influencing survival of patients with esophageal cancer in north of Iran using weibull and log-logistic regression models.
Methods: Demographic and clinical data of 359 patients with confirmed diagnosis of esophageal cancer from Babol Cancer registry utilized for our model. parametric and weibull models were employed to analyze the data. The Akaike information criterion (AIC) was also considered as a criterion to select the best model(s). All p values as 0.05 were considered as statistically significant.
Results: The sample study consisted of 62.7% men and 37.3% women. Estimated survival rates in 1, 3, and 5 years following diagnosis were 23%, 15%, and 13% respectively. According to AIC criterion, the hazard rate of non-monotonic and rejection proportional hazards assumption (p<0.05), log-logistic model was more efficient than weibull model. Family history of having cancer in patients showed a significant difference in both models.
Conclusion: It is concluded that early detection of people with a family history of cancer can be effective as an important factor in reducing the risk of death in patients with esophageal cancer.
