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Articles by Kazem Mohammad
Total Records ( 2 ) for Kazem Mohammad
  Nargess Saiepour , Kazem Mohammad , Roya Abhari , Hojjat Zeraati and Ahmad Ali Noorbala
  The aim of this study was to investigate the association between mental disorder and back pain among postmenopausal Iranian women. Three thousand six hundred and fifty five postmenopausal women were interviewed in the second National Health Survey (2nd NHS) in the year 2000, in Iran. Of whom, 2953 women were included in this study. Back pain (BKP) was considered as dependent variable and mental disorder as independent variable. Factors like age, Body Mass Index (BMI), residential area, employment, literacy, smoking habit, marital status and spinal fractures were considered as confounders. Logistic regression models have been applied for data analysis. The BKP prevalence was 40.1% and the prevalence of mental disorder was 44.3%. After adjustment for confounders, mental disorder was positively associated with BKP, OR (CI): 1.615 (1.36, 1.91). This study confirmed that BKP and mental disorder are common problems and these two factors are associated amongst postmenopausal women. Further longitudinal studies are recommended to specify casual inferences.
  Zohreh Amiri , Kazem Mohammad , Mahmoud Mahmoudi , Hojjat Zeraati and Akbar Fotouhi
  This study is designed to assess the application of neural networks in comparison to the Kaplan-Meier and Cox proportional hazards model in the survival analysis. Three hundred thirty gastric cancer patients admitted to and surgically treated were assessed and their post-surgical survival was determined. The observed baseline survival was determined with the three methods of Kaplan-Meier product limit estimator, Cox and the neural network and results were compared. Then the binary independent variables were entered into the model. Data were randomly divided into two groups of 165 each to test the models and assess the reproducibility. The Chi-square test and the multiple logistic model were used to ensure the groups were similar and the data was divided randomly. To compare subgroups, we used the log-rank test. In the next step, the probability of survival in different periods was computed based on the training group data using the Cox proportional hazards and a neural network and estimating Cox coefficient values and neural network weights (with 3 nodes in hidden layer). Results were used for predictions in the test group data and these predictions were compared using the Kaplan-Meier product limit estimator as the gold standard. Friedman and Kruskal-Wallis tests were used for comparisons as well. All statistical analyses were performed using SPSS version 11.5, Matlab version 7.2, Statistica version 6.0 and S_PLUS 2000. The significance level was considered 5% (α = 0.05). The three methods used showed no significance difference in base survival probabilities. Overall, there was no significant difference among the survival probabilities or the trend of changes in survival probabilities calculated with the three methods, but the 4 year (48th month) and 4.5 year (54th month) survival rates were significantly different with Cox compared to standard and estimated probabilities in the neural network (p<0.05). Kaplan-Meier and Cox showed almost similar results for the baseline survival probabilities, but results with the neural network were different: higher probabilities up to the 4th year, then comparable with the other two methods. Estimates from Cox proportional hazards and the neural network with three nodes in hidden layer were compared with the estimate from the Kaplan-Meier estimator as the gold standard. Neither comparison showed statistically significant differences. The standard error ratio of the two estimate groups by Cox and the neural network to Kaplan-Meier were no significant differences, it indicated that the neural network was more accurate. Although we do not suggest neural network methods to estimate the baseline survival probability, it seems these models is more accurately estimated as compared with the Cox proportional hazards, especially with today`s advanced computer sciences that allow complex calculations. These methods are preferable because they lack the limitations of conventional models and obviate the need for unnecessary assumptions including those related to the proportionality of hazards and linearity.
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