Showing 4 results for Pourhoseingholi
A Fadaee, F Mosaddegh, M Alimoradi, Ma Pourhoseingholi,
Volume 2, Issue 3 (24 2006)
Abstract
Background & Objectives: There are near two million people from around the world have planning to journey for Hadje ceremony. Old age, Crowding dormitory, close contact, poor sanitation, poor health services, different community pattern, high temperature and other factors cause the infectious diseases particularly respiratory infection more than ever Influenza occur more common and cause more discomfort during this ceremony. This study in 1381 Clear the effect of influenza vaccine compare with placebo in this group of population Pilgrimage group.
Methods: This study had interventional randomize clinical design on 156 pilgrims in Abhar City. The pattern of the cases selection for reducing of bias effect was every other pilgrim for vaccine injection. All of the cases before, during and three week after return back from this ceremony had close health services. All the pilgrims had private document for recording the events.
Results: About 147 of pilgrims experienced had pulmonary infection (93% in case and 96% in control). There was no difference in either cases or control groups
Conclusions: There is no benefit for using influenza vaccine other than its indications in the patients but it needs additional studies.
Ma Pourhoseingholi, E Hajizadeh, A Abadi, A Safaee, B Moghimi Dehkordi, Mr Zali,
Volume 3, Issue 1 (21 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.
H Noorkojuri, E Hajizadeh, Ar Baghestani, Ma Pourhoseingholi,
Volume 9, Issue 2 (Vol 9, No 2, Summer 2013 2013)
Abstract
Background & Objectives: Cox regression model is one of the statistical methods in survival analysis. The use of smoothing techniques in Cox model makes the more accurate estimates for the parameters. Fractional polynomial is one of these techniques in Cox model. The aim of this study was to assess the effects of prognostic factors on survival of patients with gastric cancer using the fractional polynomial in Cox model and Cox proportional hazards.
Methods: Information of total of 216 patients with gastric cancer who underwent surgery in the gastroenterology ward of Taleghani Hospital in Tehran between 2003 and 2008 were included in this retrospective study. In this research, fractional polynomial in Cox model and Cox proportional hazards model were utilized for determining the effects of prognostic factors on patients’ survival time with gastric cancer. The SPSS version 18.0 and R version 2.14.1 were used for data analysis. These models were compared with Akaike information criterion.
Results: The analysis of Cox proportional hazards and fractional polynomial models resulted in age at diagnosis and tumor size as prognostic factors on survival time of patients with gastric cancer independently (P<0.05). Also, Akaike information criterion was equal in both models.
Conclusion: In the present study, the Cox proportional hazards and fractional polynomial models led to similar results with equal Akaike information criterions. Using of smoothing methods helped us eliminate non-linear effects but it seemed more appropriate to use Cox proportional hazards model in medical data because of its’ ease of interpretation and capability of modeling in both continuous and discrete covariates.
Am Keshtvarz Hesam Abadi , E Hajizadeh, Ma Pourhoseingholi, E Nazemalhossein Mojarad ,
Volume 14, Issue 4 (Vol.14, No.4, 2019)
Abstract
Background and Objectives: The purpose of this study was to predict the mortality rate of colorectal cancer in Iranian patients and determine the effective factors on the mortality of patients with colorectal cancer using random forest and logistic regression methods.
Methods: Data from 304 patients with colorectal cancer registry from the Gastroenterology and Liver Research Center of Shahid Beheshti University of Medical Sciences during the years 2009 to 2014 were used as a retrospective study. Data analysis was performed using random forest and logistic regression methods. To analyze the data, R software version 3.4.3 was considered.
Results: Ten important variables related to colorectal cancer deaths were selected by random forest method. Several criteria such as the area under the characteristic curve (AUC) were used to compare the random forest method with logistic regression. According to both criteria, five important variables ranked by random forest were Cancer stage, age of diagnosis, patient's age, HLA, and degree of differentiation (tumor differentiation). In terms of different criteria, the random forest method had better performance than logistic regression (Area under the ROC curve for random forest and logistic regression methods was: 98%; 80% respectively).
Conclusion: Variables such as Cancer stage, age of diagnosis, patient's age, HLA, and degree of differentiation are considered as the most important factors affecting mortality in colorectal cancer, that the patients' longevity can be increased with the early diagnosis of cancer and screening programs.
