Iranian Journal of

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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.

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Background & Objectives: Cholera is always being considered as a public health threat in poor and developing countries. However outbreaks of cholera are not very common in central area of Iran in 2008 district health authority reported a cluster of diarrhea cases. We investigated this cluster to identify the etiological agent, source of transmission and propose control measures.
Methods: We analyzed the data of total of 1219 patients with colorectal cancer who registered between 1 January 2002 to 1 October 2007. Data were analyzed using univariate and multivariate Accelerated Failure Time (AFT) parametric survival model with frailty, utilizing STATA statistical software.
Results: In the univariate analysis for age at diagnosis, gender, marital status, race and education level, the survival of patients with colon cancer were approximately between half to one fourth and for BMI, alcohol history, Inflammatory Bowel Disease (IBD), familial history of cancer and the pathologic stage of tumor, the survival of patients with colon cancer were significantly (between 0.12 to 0.56 times) shorter than those patients with rectal cancer. In the multivariate analysis, for age at diagnosis (45-65 years), there was significant difference between colon and rectum cancer. But for BMI, alcohol history, IBD and pathologic stage there were not significant differences. The adjusted survival and 1, 2, 3, 4 and 5 year survival of patients with rectal cancer were better than those with colon cancer.
Conclusions: Site-specific evaluation of colon and rectum could give a better perspective of factors affecting these cancers. It may help to design of clinical trials, better diagnosis of diseases and optimal administration of specific treatments.

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Background & objectives: Competing risk data is one of the multivarite survival data. Competing risk data can be modelled using copula function. In this study we propose a bayesian modelling approach of competing risk data using the copula function.
Methods: We used the data from colorectal cancer registyrarty in Tehran. After constructing likelihood function using Clayton copula by choosing appropriate prior distribution for parameters, we obtained the posterior distribution of parameters using the Metropolis-Hastings algorithms and Slice sampling.
Results: The results of univariate analysis showed that sex, histology of tumor, extent of wall penetration, lymph node metastasis, distant metastasis and pathological stage of tumor were significantly associated with colon cancer and sex, histology of tumor, lymph node metastasis, distant metastasis and pathological stage of tumor were were significantly related to rectal cancer. In the multivariate analysis, age at diagnosis, tumor grade and distant metastasis were significant prognostic factors for colon cancer and tumor grade and size of the tumor were significant prognostic factors of rectal cancer
Conclusions: As we showed some variables may have different impacts on colon and rectum cancers, consequently, further studies are needed to be conducted considering risk factors of these cancers separately.

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Background & Objectives: Identifying the important influential factors is a great challenge in oncology studies. Decision tree is one of methods that could be used to evaluate the prognostic factors and classifying the patients' homogeneously. This method identifies the main prognostic factors and then determines the subgroups of patients based on those prognostic factors. The aim of this study was to assess the prognostic factors and homogeneous subgroups of colorectal patient through survival tree.
Methods: Data collected from an observational of 739 colorectal patients registered in the cancer registry affiliated to the center of Research Center of Gastroenterology and Liver Disease (RCGLD), Shahid Beheshti Medical University, Tehran, Iran. Death was the interested event and the survival time was calculated from date of diagnosis until occurrence of event (or censoring) in months. Finally we used decision tree based method for classifying and analyzing the data.
Results: Based on our result, decision tree identified four covariates as important prognostic factors in 0.05 significant levels: general stage of cancer, age of diagnosis, histology of tumor and morphology type of tumor. Also patients based on these prognostic factors divided into five homogeneous subgroups. The greater values of measure of separation (SEP) criterion support the appropriateness of this model for such the data.
Conclusion: Decision tree is powerful and intuitive method. It has a key feature that is in addition to evaluate the prognostic factors, provides the homogeneous subgroups for future analysis.