Showing 14 results for Bayes
A Akbarzadeh Bagheban, A Beaji, Y Mehrabi, H Saadat,
Volume 5, Issue 3 (12-2009)
Abstract
Background and objective: Numerous studies have reported beneficial effects of smoking cessation in terms of decreased cardiovascular mortality in patients with coronary heart disease. This paper aimed to determine a valid estimate for the relative risk of mortality in subjects who quit smoking compared to those continued smoking.
Methods: All relevant prospective cohort studies of chronic heart disease published during 1975 to 2008 were considered. Studies with at least two years follow-up were eligible for analysis. The qualities of studies were assessed independently by two reviewers. In addition, to obtain a precise estimate, we used the sample size and the follow-up duration of each study as the covariates in the Bayesian meta-analysis model. The Winbugs and Boa softwares were utilized for fitting the Bayesian meta-analysis model.
Results: The estimate of relative risk of mortality for those who quit smoking compared to those continued smoking was 0.64 (95%CI: 0.57-0.70). We also did not find any significant relationship between the estimate of risk reduction and the described covariates.
Conclusions: Using this Bayesian meta-analysis, a 36% reduction in relative risk of mortality was found for those who quit smoking compared to those continued smoking, after eliminating the effects of study sample size and follow-up duration.
Y Mehrabi, E Maraghi, H Alavi Majd, Me Motlagh,
Volume 6, Issue 3 (12-2010)
Abstract
Background and objective: Disease or mortality mapping are statistical methods aimed at providing precise estimates of rates across geographical maps. The aim of this research is to improve the precision of relative risk (RR) estimates of infant mortality (IM) for different rural areas, using empirical and full Bayesian methods.
Methods: Infant mortality data were extracted from the vital horoscope (Zij-Hayati) for years 2001 and 2006 across rural areas of Iran. Maximum Likelihood, Empirical Bayes with Poisson-Gamma model and full Bayesian models were used. Mont Carlo Markov Chain method was used for latter models. Deviance information criterion (DIC) was computed to check the models fittings. R, WinBUGS and Arc GIS software were employed.
Results: Based on the full Bayesian method, the highest RR of infant mortality was 1.73 (95%CI: 1.58-1.88) in year 2001 and 1.62 (95%CI: 1.50-1.75) in 2006 which belonged to Sistan-va-Blouchestan area in comparison to the whole country. In 2001, the rural areas of Birjand (1.45), Kordistan (1.23) and Khorasan (1.21) and in 2006, Birjand (1.42), Zanjan (1.39), Kordistan (1.36), Ardebil (1.32), Zabol (1.28), West Azerbaijan (1.18) and finally Golestan (1.14) had significant RR of IM (all p<0.05). The lowest RR of infant mortality for year 2001 were belong to rural areas of Tehran University (0.56) and for year 2006 to former Iran University (0.52).
Conclusion: To estimate the mortality map parameters, the full Bayesian method is preferred compared to empirical Bayes and maximum likelihood.
Ar Baghestani, E Hajizadeh, Sr Fatemi,
Volume 6, Issue 3 (12-2010)
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.
Ma Akhoond, A Kazemnejad, E Hajizadeh, Sr Fatemi, A Motlagh,
Volume 6, Issue 4 (3-2011)
Abstract
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.
M Cheharazi, M Shamsipour, M Norouzi, F Jafari, F Ramazan Ali,
Volume 8, Issue 2 (9-2012)
Abstract
Background & Objectives: One of the problems of diagnostic accuracy studies is
verification bias. It occurs when standard test performed only for
non-representative subsample of study subjects that diagnostic test done for
them. In this study we extend a Bayesian method to correct this bias.
Methods: Patients
that have had at least twice repeated failures in cycles IVF ICSI were included
in this model. Patients were screened by using an ultrasonography and those
with polyps recommended for hysteroscopy. A logistic regression with binomial
outcome fit to predict the missing values (false and true negative),
sensitivity and specificity. Bayesian methods was applied with informative
prior on polyp prevalence. False and true negatives were estimated in Bayesian
framework.
Results: A
total of 238 patients were screened and 47 had polyps. Those with polyps are
strongly recommended to undergo hysteroscopy, 47/47 decided to have a
hysteroscopy and 37/47 were confirmed to have polyps. None of the 191 patients
with no polyps in ultrasonography had hysteroscopy. The false negative was
obtained 14 and true negative 177, so sensitivity and specificity was estimated
easily after estimating missing data. Sensitivity and specificity were equal to
74% and 94% respectively.
Conclusion: Bayesian analyses with
informative prior seem to be powerful tools in simulation experimental
M Gholami Fesharaki , A Kazemnejad, F Zayeri, J Sanati, H Akbari,
Volume 8, Issue 4 (3-2013)
Abstract
Background and Objectives: Since there is inconsistency reports in relationship between shift work (SW) and blood pressure (BP), therefore we aimed to show any association between SW and BP by using of Bayesian Multilevel Modeling, which is a reliable method for this type of analysis.
Methods: The profiles of 4145 workers in Polydactyl Iran Corporation were examined in historical cohort between 1996 until 2008. All relevant analysis was performed by Win Bugs software.
Results: Approximately 98 percent of study population was male. Of total 1886 (45.5%), 307(7.4%), 1952 (47.1%) of participation were day worker, two rotation shift worker and three rotation shift worker respectively. After controlling confounding factors, there was no significant relationship with Systolic BP (P=0.911) and Diastolic BP (P=0.278).
Conclusion: In general, the results of our historical cohort study do not support a relationship between SW and BP. We suggest multi center and prospective cohort studies with controlling more confounding factors in this area.
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.
M Gholami Fesharaki , A Kazemnejad , F Zayeri , M Rowzati, H Akbari,
Volume 10, Issue 4 (3-2015)
Abstract
Background and Objectives : Previous studies have reported contradictory results regarding the association of Shift Work (SW) and Blood Cholesterol (BC). In this paper, we studied the relationship between SW and BC.
Methods: The data of this historical cohort study was extracted from annual observations of the workers of Esfahan’s Mobarakeh Steel Company selected through cluster random sampling between 1996 and 2011. In this research, we assessed the effect of SW on BC with controlling BMI, age, work experience, marital status, smoking, and educational status.
Results : Five hundered and seventy four male workers participated in this study with a mean (SD) age of 41.89 (7.51) and mean (SD) work experience of 16.75 (7.16) years. In this study, after controlling confounding factors, we found no significant relationship between SW and BC.
Conclusion: Because our study showed no relationship between SW and BC, we can state that this relationship does not exist with more certainty.
N Zare, S Khodarahmi, A Rezaianzadeh,
Volume 11, Issue 3 (11-2015)
Abstract
Background and Objectives: Breast cancer is one of the most common cancers among women and is the second main cause of death after lung cancer. The objective of this study was to use the Bayes model to analyze the prognostic effects on the survival of the women with breast cancer after surgery in the south of Iran.
Methods: The date was collected 1192 women who had breast cancer in Namazi Hospital Research Center between 2001 and 2006. The complete information of only 1148 of them was registered. Parametric Bayes and Bayes Cox methods were used. Considering 0.05 as the level of significance, the data analysis was done using the WinBUGS14 software.
Results: The mean age of the patients (at the time of diagnosis) was 47 years in this study. Cox one-variable analysis showed a significant relationship between survival and smoking (P=0.009), bone metastasis (P=0.01), the number of lymph nodes (P=0.001), the tumoral level of malignancy (P=0.001), the surgical method (P=0.015), financial status (P=0.025), and the tumor size (P=0.001). By fitting Bayes models the variables tumor size, level of malignancy and number of lymph nodes were significant.
Conclusion: The results showed that clinicopathological features of cancer had a significant role in the survival of the patients.
Sh Seyedagha, A Kavousi , Ar Baghestani , M Nasehi,
Volume 13, Issue 3 (12-2017)
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.
S Heidari, A Kavousi, V Rezaei Tabar,
Volume 14, Issue 2 (9-2018)
Abstract
Background and Objectives: Breast cancer is the most common cancer in Iran. It can be prevented by rapid diagnosis of the disease. Thus, it is necessary to determine the causal relationships between variables related to breast cancer. Bayesian network is a data mining tool that shows the causal relationship between different variables. In this paper, a Bayesian network was applied to find causal relationships between breast cancer variables using a genetic algorithm in a graphical model.
Methods: in this applied study, data were collected from 900 breast cancer patients in Kerman Province from 1999 to 2008. For data analysis, we used a probabilistic graphical model representing the causal relationship between variables.
Results: The results showed that surgery was the most important treatment for breast cancer. Based on the conditional and marginal probabilities, the women who underwent surgery had higher hopes of living longer. Moreover, 81% of the patients who did not undergo surgery only received chemotherapy or radiotherapy were less likely to have long lives.
Conclusion: People aged 40-65 years are more likely to have breast cancer. Moreover, the variables of age, surgery, chemotherapy, and radiotherapy had a direct effect on the status of the patients and there were direct edges from these variables to the status of the patients.
F Feizmanesh, Aa Safaei,
Volume 14, Issue 3 (12-2018)
Abstract
Background and Objectives: Pulmonary embolism is a potentially fatal and prevalent event that has led to a gradual increase in the number of hospitalizations in recent years. For this reason, it is one of the most challenging diseases for physicians. The main purpose of this paper was to report a research project to compare different data mining algorithms to select the most accurate model for predicting pulmonary embolism in hospitalized patients. This model would provide the knowledge needed by the medical staff fir better decision making.
Methods: In this research, we designed a prediction model using different methods of machine learning that would best predict the probability of pulmonary embolism in patients at risk. Among data mining algorithms, Bayesian network, decisions tree (J48), logistic regression (LR), and sequential minimal optimization (SMO) were used. The data used in the study included risk factors and past history of patients admitted to the Lung Department of Shariati Hospital, Tehran, Iran.
Results: The results showed that the accuracy and specificity of all prediction models were satisfactory. The Bayesian model had the highest sensitivity in predicting pulmonary embolism.
Conclusion: Although the results showed a little difference in the performance of prediction models, the Bayesian model is a more appropriate tool to predict the occurrence of pulmonary embolism in hospitalized patients in this type of data. It can be considered a supportive approach along medical decisions to improve disease prediction.
M Amini, A Kazemnejad, F Zayeri, A Amirian, N Kariman,
Volume 16, Issue 1 (6-2020)
Abstract
Background and Objectives: Gestational diabetes mellitus (GDM) is a medical problem in pregnancy, and its late diagnosis can cause adverse effects in the mother and fetus. The purpose of this research was to estimate the accuracy parameters of a biomarker for early prediction of gestational diabetes in the absence of a perfect reference standard test.
Methods: This study was conducted in 523 pregnant women who presented to Mahdieh Hospital and Taleghani Hospital affiliated with Shahid Beheshti University of Medical Sciences, Tehran, Iran 2017-2018. As a predictor for detecting GDM, beta- human chorionic gonadotropin (β-hCG) measurements were recorded during 14-17th weeks’ gestation in a checklist. The Bayesian latent variable model was used to estimate the sensitivity, specificity, and area under receiver operating characteristic curve (AUC). Bayesian parameter estimation was calculated using the R2OpenBUGS package in R version 3.5.3.
Results: The median gestational age was 33 years. In the absence of a perfect reference test, the applied model had a sensitivity, specificity, and AUC of 78% (95% credible interval (CrI): 0.66-0.83), 83% (95% CrI: 0.74-0.89), and 0.72 (95% CrI: 0.64-0.88) for β-hCG, respectively.
Conclusion: According to the results of this study, β-hCG may be an acceptable biomarker for early diagnosis of diabetes in pregnant women in the absence of a perfect reference test.
A Naghi Pour, A Moghimbeigi, N Shirmohamadi, A Soltanian, S Khazaei, Sh Nick Ceiar,
Volume 17, Issue 4 (3-2022)
Abstract
Background and Objectives: Breast cancer has the highest incidence in the Iranian women.
Methods: A cross-sectional study was conducted. All female with breast cancer during 2008-2015 were enrolled. Breast cancer registration is based on the pathology method in Iran. The information about female with breast cancer was collected from their files in the cancer registry department of Hamadan Health Center. The samples were divided into four groups according to age (<50 and> 50) and location (city, village). GeoBUGS was used to generate a map of high-risk areas in Hamedan Province based on the adjusted relative risk estimate (RR*) in OpenBUGS v 3.2.3 software.
Results: This study included 1316 females with breast cancer. The mean age of the patients was 50.38±12.98 years. The results of the study showed that high-risk areas of breast cancer for were Assadabad urban females aged over 50 years (RR*(i)=1.32, CI= 0.99,1.79) and Tuyserkan (RR*(i)=1.09, CI= 1.08,1.38) and Razan (RR*(i)=1.09, CI= 0.85,1.40) for females below 50 years. In addition, Razan for rural females over 50 years old (RR*(i)=1.18, CI=0.82,1.73) and Malayer for females below 50 years old (RR*(i)=1.08, CI= 0.81,1.45) were high risk areas for breast cancer in Hamadan Province.
Conclusion: The distribution of breast cancer is different at different ages and in the cities of Hamadan Province. Asadabad, Tuyserkan, Razan and Malayer were high risk areas for breast cancer in Hamadan Province.
