Showing 3 results for Parametric Model
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.
Ar Baghestani, E Hajizadeh, Sr Fatemi,
Volume 6, Issue 3 (12-2010)
Abstract
Background & Objectives: The Cox proportional-hazards regression and other parametric models model have achieved widespread use in the analysis of time-to-event data with censoring and covariates. However employing Bayesian method has not been widely used or discussed. The aim of this study was to evaluate the prognostic factors in using Bayesian interval censoring analysis.
Methods: This cohort study was based on 178 patients with gastric cancer from January 2003 to December 2007 admitted to Taleghani teaching hospital in Tehran. Known prognostic risk factors were entered into the analysis using Bayesian Weibull and Exponential models. The term DIC was employed to find best model.
Results: The results were showed survival rate depended on age of diagnosis and tumor size. Those patients who had early diagnosis and/or had smaller tumor size were in lower risk of death.
Conclusion: The age of diagnosis and tumor size of patients are important prognostic factors related to survival of patients with gastric cancer. Based on DIC, Bayesian analysis of the Weilbull model performed better than the Exponential model. As a result, if this cancer has been diagnosed early, the relative risk of death would reduce.
A Biglarian, E Hajizadeh, A Kazemnejad,
Volume 6, Issue 3 (12-2010)
Abstract
Background & Objective: Using parametric models is common approach in survival analysis. In the recent years, artificial neural network (ANN) models have increasingly used in survival prediction. The aim of this study was to predict of survival rate of patients with gastric cancer by using a parametric regression and ANN models and compare these methods.
Methods: We used the data of 436 gastric cancer patients from a cancer registry in Tehran between 2002-2007. All patients had a confirmed diagnosis. Data were randomly divided into two groups: training and testing (or validation) set. For analysis of data we used a parametric model (exponential, Weibull, normal, lognormal, logistic and log-logistic models) and a three layer ANN model. In order to compare of the prediction of two models, we used the area under receiver operating characteristic (AUROC) curve, classification table and concordance index.
Results: The prediction accuracy of the ANN and the parametric (Weibull) models were 79.45% and 73.97% respectively. The AUROC for the ANN and the Weibull models were 0.815 and 0.748 respectively.
Conclusions: The ANN had a better predictions than the Weibull model. Thus it is suggested to use of the ANN model survival prediction in field of cancer.
