Volume 6, Issue 3 (11 2010)
irje 2010, 6(3): 18-21 |
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Baghestani A, Hajizadeh E, Fatemi S. To Evaluate the Prognostic Factors in Using Bayesian Interval Censoring Analysis on Survival Rate of Gastric Cancer in Iran. irje 2010; 6 (3) :18-21
URL: http://irje.tums.ac.ir/article-1-73-en.html
URL: http://irje.tums.ac.ir/article-1-73-en.html
Abstract: (15238 Views)
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.
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.
Keywords: Bayesian method, interval censoring, parametric model, gastric cancer, Deviance information criteria
Type of Study: Research |
Subject:
General
Received: 2009/08/17 | Accepted: 2010/07/3 | Published: 2013/09/1
Received: 2009/08/17 | Accepted: 2010/07/3 | Published: 2013/09/1
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