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Golestan province has a reputation for relatively high incidence rates of gastric cancer in Iran. Along with dietary, lifestyle and environmental influential factors, soil selenium and high levels of pesticide used may exert influence in this region. The present study was designed for modeling the influential predictors on incidence of gastric cancer in Golestan. All registered cases of gastric cancer from March 2009 to March 2010 (49 females and 107 males) were investigated. Data were gathered by both check list and researcher made questionnaire (demographic, clinical and lifestyle characteristics) and analysed using logistic regression. Mean (±SD) age at diagnosis was 62.9±13.8 years. CIR and ASR of gastric cancer showed 9.16 and 13.9 per 100,000 people, respectively. Based on univariate logistic regression, a history of smoking (OR= 2.076), unwashed hands after defecation (OR= 2.612), history of cancer in relatives (OR= 2.473), history of gastric cancer in first-degree relatives (OR= 2.278), numbers of gastric cancers in first-degree relatives (OR= 2.078), history of X-ray and dye exposure (OR= 2.395), history of CT scan encounter (OR= 2.915), improper food habits (OR= 3.320), specific eating behavior (OR= 0.740), consumption of probable high risk foods (OR= 2.942), charred flesh (OR= 1.945), and animal fat (OR= 2.716) were confirmed as a risk factors. Changes in lifestyle may be expected to increase gastric cancer incidence dramatically in the near future. Therefore, appropriate educational interventions should be designed and implemented by competent authorities.

Background and Objectives: Gastric cancer is the second leading cause of cancer death worldwide and is the most common type of cancer in Iran. The objective of this research was to apply additive hazards models to the study of survival of patients with gastric cancer and to compare with results obtained using the Cox model., Methods: We retrospectively studied 213 patients with gastric cancer who were registered in one referral cancer registry center in Tehran, Iran. Age at diagnosis, sex, presence of metastasis, tumor size, histology type, lymph node metastasis, and pathologic stages were entered into analysis using the Cox model and additive hazard models. To visualize a covariate effect over time, the estimated cumulative regression function by the Aalen's model was examined., Results: The five-year survival rate and the median life expectancy in the studied patients were 14.6% and 29.6 months, respectively. Multivariate Cox and Additive hazards models analysis identified age at diagnosis, tumor size and pathologic stage as independent prognostic factors for the survival of patients with gastric cancer. Moreover, pathologic stage had a late or delayed effect according to the Aalen's plot. Other clinicopathological characteristics were not statistically significant., Conclusion: Since Cox and Aalen models give different aspects of the association between risk factors and the study outcome, it seems desirable to use then together to give a more comprehensive understanding of data. Our results also suggest that early detection of patients at younger age and in primary stages is important to increase survival of patients with gastric cancer.

Background and Objectives: Gastric cancer is the second leading cause of cancer death worldwide and is the most common type of cancer in Iran. The objective of this paper is to apply the additive hazards models to the study of survival of patients with gastric cancer and to compare results obtained by the additive hazards models and the Cox model., Methods: We retrospectively studied 213 patients with gastric cancer who were registered in one referral cancer registry center in Tehran, Iran. Age at diagnosis, sex, presence of metastasis, tumor size, histology type, lymph node metastasis, and pathologic stages were entered into analysis using the Cox model and additive hazard models. To visualize a covariate effect over time, the estimated cumulative regression function by the Aalen's model is examined., Results: The five-year survival rate and the median life expectancy in the studied patients were 14.6% and 29.6 months, respectively. Multivariate Cox and Additive hazards models analysis identified that age at diagnosis, tumor size and pathologic stage were independent prognostic factors for the survival of patients with gastric cancer (P<0.05). Moreover, pathologic stage has a late or delayed effect according to the Aalen's plot. Other clinicopathological characteristics were not statistically significant (P<0.05)., Conclusion: Since Cox and additive models give different aspects of the association between risk factors and the study outcome, it seems desirable to use together to give a more comprehensive understanding of data. Our results also suggest that early detection of patients at younger age and in primary stages is important to increase survival of patients with gastric cancer.

Aims and Background: The objective of the study was to assess the impact of prognostic factors on survival of patients with gastric cancer in the presence of interval censoring using parametric models., Methods and Study Design: In a retrospective cohort study, 178 patients with gastric cancer were studied from February 2003 to January 2008. Gender, age at diagnosis, distant metastasis, tumor size, histology type, tumor grade, lymph node metastasis and pathologic stage were selected as prognostic and entered in the models. Weibull, exponential, log-logistic and log-normal analyses with interval censoring were performed as parametric models, and Akaike Information Criterion (AIC) was used to compare the efficiency of models., Results: The risk of death for patients at an older age, with tumor size greater than 35 mm, distant metastasis and advanced stage of disease was statistically higher. Other clinical and demographic factors were not significant. According to AIC, the log logistic model is the most efficient of all the models in multivariable analysis., Conclusions: The results indicated that the early detection of a cancer at a young patient age and in primary stages is important to increase survival from gastric cancer. According to statistical criteria, a parametric model can also be a useful statistical model to find prognostic factors in the presence of interval censoring. Although it seems that all models in this analysis fit well, AIC supported the log logistic regression as the best option.

Background and Objectives: The aim of this study is to determine diagnostic factors for Iranian gastric cancer patients and their importance using artificial neural network and Weibull regression models., Methods: This study was a historical cohort study with data gathered from 436 registered gastric cancer patients who underwent surgery between 2002 and 2007 at the Taleghani Hospital (a referral center for gastrointestinal cancers), Tehran, Iran. In order to determine risk factors and their importance, neural network and Weibull regression models were used., Results: The Weibull regression analysis showed that lymph node metastasis and histopathology of tumor were selected as important variables. Based on the neural network model, staging, lymph node metastasis, histopathology of tumor, metastasis, and age at diagnosis were selected as important variables. The true prediction of neural network was 82.6%, and for the Weibull regression model, 75.7%., Conclusion: The present study showed that the neural network model is a more powerful tool in determining the important variables for gastric cancer patients compared to Weibull regression model. Therefore, this model is recommended for determining of risk factors of such patients.

Background & Objective: Gastric cancer is one of the most common cancers in the world. It is rarely detected early, and the prognosis remains poor. Cox proportional hazard model is used to examine the relationship between survival and covariates. Parametric survival models such as log normal regression model can also be used for this analysis. We used log normal regression model in this study to evaluate prognostic factors in gastric cancer and compared with Cox model., Methods: We retrospectively studied the 746 patients diagnosed with gastric cancer admitted in a referral hospital in Tehran, Iran, from February 2003 through January 2007. Age at diagnosis, sex, extent of wall penetration, histology type, tumour grade, tumour size, pathologic stage, lymph node metastasis and presence of metastasis were entered into a log normal model. Hazard rate (HR) was employed to interpret the risk of death and the results were compared with Cox regression. The AIC (Akaike Information Criterion) was employed to compare the efficiency of models., Results: Univariate analysis indicated that with increasing age the risk of death increased significantly in both log normal and Cox models. Patients with greater tumour size were also in higher risk of death followed by those with poorly differentiated and moderately differentiated in tumour grade and advanced pathologic stage. The presence of metastasis was significant prognostic factor only in log normal analysis. In final multivariate model, age was still a significant prognostic factor in Cox regression but it was not significant in log normal model. Presence of metastasis followed by histology type were other prognostic features found significant in log normal results. Based on AIC, log normal model performed better than Cox., Interpretation & Conclusion: Our results suggest that early detection of patients in younger age and in primary stages and grade of tumour could be important to decrease the risk of death in patients with gastric cancer. Comparison between Cox and log normal models indicated that log normal regression model can be a useful statistical model to find prognostic factors instead of Cox.

Background and Objectives: Gastric cancer is one of the most common cancers in the world. The aim of this study was to evaluate prognostic factors using Bayesian interval censoring analysis., Methods: This is a historical cohort study of 178 patients from February 2003 through January 2008, admitted with gastric cancer to one referral hospital in Tehran. Age at diagnosis, sex, histology type, tumor grade, tumor size, pathologic stage, lymph node metastasis and distant of metastasis were entered into the analysis using Bayesian Weibull and Exponential models. The term DIC was employed to find best model., Results: The results showed that as age increased, the risk of death slightly increased significantly in both Weibull and Exponential models with similar results. Patients with grater tumor size were also in higher risk of death followed by advanced pathologic stage. Neither the Weibull nor the Exponential models found sex, distant metastasis, histology type, tumor grade and lymph node metastasis to be prognostic factors. Based on DIC, Bayesian analysis of the Weibull model performed better than the Exponential model., Conclusion: According to these results the early detection of patients at lower ages and in primary stages is important to increase the survival in cases with gastric cancer.

Background: Researchers in medical sciences often tend to prefer Cox semi-parametric instead of parametric models for survival analysis because of fewer assumptions but under certain circumstances, parametric models give more precise estimates. The objective of this study was to compare two survival regression methods - Cox regression and parametric models - in patients with gastric adenocarcinomas who registered at Taleghani hospital, Tehran., Methods: We retrospectively studied 746 cases from February 2003 through January 2007. Gender, age at diagnosis, family history of cancer, tumor size and pathologic distant of metastasis were selected as potential prognostic factors and entered into the parametric and semi parametric models. Weibull, exponential and lognormal regression were performed as parametric models with the Akaike Information Criterion (AIC) and standardized of parameter estimates to compare the efficiency of models., Results: The survival results from both Cox and Parametric models showed that patients who were older than 45 years at diagnosis had an increased risk for death, followed by greater tumor size and presence of pathologic distant metastasis., Conclusion: In multivariate analysis Cox and Exponential are similar. Although it seems that there may not be a single model that is substantially better than others, in univariate analysis the data strongly supported the log normal regression among parametric models and it can be lead to more precise results as an alternative to Cox.