Showing 5 results for Baghestani
Ar Baghestani, E Hajizadeh, Sr Fatemi,
Volume 6, Issue 3 (11 2010)
Abstract
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.
F Mohammadzadeh, S Faghihzadeh, Ar Baghestani, M Asadi Lari , Mr Vaez Mahdavi, J Arab Kheradmand , Aa Noorbala, Mm Golmakani, Aa Haeri Mahrizi , R Kordi,
Volume 9, Issue 1 (5-2013)
Abstract
Background & Objectives: Chronic pain is one of main public and individual health problems and its epidemiological understanding needs reliable estimates of prevalence. The aim of this study was to investigate the epidemiology of chronic pain in all 368 neighborhoods of Tehran using small area estimation method.
Methods: The pain section from the second round of Urban HEART data from a selected individual of 23457 households in Tehran using a multistage randomized cluster sampling in 2011, were analyzed. In order to obtain reliable estimates for chronic pain prevalence at neighborhood level, a generalized linear mixed model and hierarchical Bayesian approach were used and the reliability of the estimates were evaluated.
Results: The average of estimated prevalence of chronic pain in neighborhoods of Tehran was 25.5% and a large heterogeneity was observed in its prevalence in neighborhoods of Tehran. Prevalence of chronic pain was significantly higher in married housewives, retirees and pensioners and was significantly associated with age, educational status, depression and anxiety (P<0.05). The reliability of Bayesian method was confirmed by evaluation methods in this analysis.
Conclusion: These results demonstrate prevailing amount of chronic pain at neighborhood-level in Tehran, which warrants careful attention to prevention, treatment, and rehabilitation by health care professionals.
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.
Ar Baghestani, A Teymourpour, Y Bashiri,
Volume 11, Issue 1 (Vol 11, No 1 2015)
Abstract
Background & Objectives: In the analysis of mortality trend in the Iran that is basically a sequences of observations sorted by time, there is a point where the statistical properties of the mortality trend change so that the first k0 observations have a distribution of F0 and other n-k0 observations have a distribution of F1. The point k0 is unknown and called the change point. The aim of this paper was to detect the location of the change point and estimate it in the real mortality data of the country.
Methods: In this study, Xt indicated the number of mortality in time t (year) and because of the numeric nature of the variable, we considered the Poisson model for the variable Xt.
We assumed in early years (t0), Xt had a Poisson distribution with a mean of ʎ0 and for later years (t>k0), Xt had a Poisson distribution with a mean of ʎ1 .In theory, we used the MIC method, a modification of the SIC method. For detecting more than one change point, we used the binary segmentation process in the mortality trend.
Results: The results showed that the change point occurred in 1993 and 1997.
Conclusion: The finding of this study showed three periods with different rates in the mortality trend of Iran.
Sh Seyedagha, A Kavousi , Ar Baghestani , M Nasehi,
Volume 13, Issue 3 (Vol.13, No.3, Atumn 2017)
Abstract
Background and Objectives: Tuberculosis is the most common cause of death among single-factor infectious diseases and is the tenth cause of death among all diseases in the world. The disease is spread mainly from an infected person through close contact with other people living in one place. The aim of this study was to investigate the relationship between the spatial correlation structure and the recovery time of patients with pulmonary tuberculosis in Iran.
Methods: In this applied study, the data of 20554 patients with sputum smear-positive pulmonary tuberculosis in Iran from 1389 to 1393 were used. A parametric accelerated failure time model with spatial frailty and batesian approach was used to analyze the data. The OpenBUGS 1.4 was used for programming and the ArcGIS 9.2 was used for mapping the environmental impact on tuberculosis.
Results: The mean age of the patients was 50.35 years with a standard deviation of 21.6 years. The results showed that the geographical environment, gender, prison condition, degree of smear positivity at diagnosis and location (urban-rural) had a significant impact on the recovery time of pulmonary tuberculosis patients. The recovery time of patients with smear grade 1-9 bacilli, 1+ and 2+ who were treated was significantly shorter than the others.
Conclusion: According to the study, geographical environment and the location have a significant impact on smear positive patients’ recovery time. This impact depends on the degree of smear positivity in some provinces and is independent of it in some other provinces.
