bayesian method

In the female population, breast cancer is the most common cancer and a leading cause of cancer death. This study was designed to investigate the geographical pattern of breast cancer risk in different counties of Fars province in the south of Iran from 2001 to 2018. In this historical cohort study, data of Shiraz Population-Based Cancer Registry between 2001 and 2018 was used. The geographical variations of breast cancer incidence rate in 36 counties of Fars province were analyzed using the Bayesian spatiotemporal model.  Overall, the averages of relative risk (RR), temporal trend (TT), and δi for breast cancer were 1.59, 1.025, and 0.00 in the total female population; 1.21, 1.002, and 0.00 in the young female population (under 40 years of age); and 1.54, 1.02, and 0.00 in the female population with invasive ductal carcinoma (IDC), respectively. The steady increase in RR of breast cancer and IDC during 2001-2018 was observed in most counties located in the non-central part of the Fars geographic map. Moreover, a steady increase of young breast cancer RR was observed mainly in southern regions and some northern cities of Fars province.  Between 2001 and 2018 in Fars province, a steady annual increase of approximately 2% was observed in the total female population for all types of breast cancer, including IDC. High-risk areas, TTs, and changing patterns of breast cancer incidence were determined in this region. Furthermore, areas with a high risk of young breast cancer were identified, which requires special attention.

Noise pollution from various internal and external sources affects people's behaviors and job performance. The current study investigated the effects of noise pollution on annoyance, aggression, and cognitive failures.

This descriptive study was conducted in 2020-2021 recruiting 400 employees of Tejarat Bank in Tehran. First, questionnaires of cognitive failures, noise annoyance, sensitivity, aggression, and demographic information were completed by the study staff. Next, association between the variables were determined. Finally, using Bayesian models, the association between variables were modeled and important factors were identified using sensitivity analysis.

Based on the results, the mean scores of noise exposure, annoyance, sensitivity, aggression and cognitive failures were 62.86 ± 6.66, 57.74 ± 23.47, 68.26 ± 17.94, 71.19 ±12.68, and 46.83 ± 12.00, respectively. Of all the variables, only annoyance and noise sensitivity had significant effect on aggression. The factors of accuracy, precision, and recall of the Bayesian model were 0.8, 0.89, and 0.96, respectively, which indicates the appropriate diagnostic performance of the model.

Based on our findings, it can be concluded that noise annoyance increases the likelihood of cognitive failures, so that the highest probability of cognitive failures occurs when people are annoyed. In addition, because people with higher noise exposure and higher education experience more annoyance, it can be concluded that the variables of education and noise exposure cause cognitive failures through annoyance.

Underestimation is a common problem in cancer registries in developing countries. This study introduces a Bayesian approach as a method for correcting undercounts in cancer data, before population-based cancer registry program

The current study is a secondary study performed on data from the cancer registry system. Our analysis focused on utilizing data before the establishment of the population-based cancer registry program in Iran. We employed the Bayesian approach to correct undercounting from 2005 to 2010. The ratio of pathology to population-based in the cancer registry data of Golestan province for four age groups and each year was used as the initial value in the Bayesian method.

The results of this study showed that the lowest percentage of undercounting belonged to Khorasan Razavi province with an average of 21% and the highest percentage belonged to Sistan and Baluchestan province with an average of 38%.
The average age-standardized incidence rate (ASR) for all provinces of the country except Golestan province was equal to 105.72 per 100,000 and after Bayesian correction was 137.17 per 100,000. In 2010 the amount of ASR before Bayesian correction was 100.28 per 100,000 for women and 136.49 per 100,000 for men. Also, after implementing the Bayesian correction, ASR increased to 125.74 per 100,000 for women and 172.79 per 100,000 for men.

The study demonstrates the effectiveness of the Bayesian approach in correcting undercounting in cancer registries. By utilizing the Bayesian method, the average ASR after Bayesian correction with a 29.74 percent change was 137.17 per 100,000. These corrected estimates provide more accurate information on cancer burden and can contribute to improved public health programs and policy evaluation. The findings of this research highlight the appropriateness of using the Bayesian method to correct underestimation in cancer registries and underscore its significance for future studies.

The COVID-19 pandemic had caused unexpected strain on healthcare systems in most countries in 2020. Although different survival models were used in clinical decision-making for COVID-19 patients, the effect of different risk factors in patients has not been identified clearly. Elderly patients, especially with comorbidities, were introduced as the most susceptible group at the risk of death. This study aimed to determine the threshold of age that influences chronic diseases and other factors that increase the cure rate of COVID-19 patients. This observational study was conducted at Shahid Sadoughi hospital in Yazd, Iran. All participants were older than 18 years old with confirmed COVID-19 and completed the day-30 and day-180 follow-ups. The Bayesian method was used through the cure rate models, practical models in survival with a single change-point to detect the threshold of age, illustrating each risk factor’s effect on the cure rate of patients. The analysis included 901 confirmed COVID-19 cases with a mean age of 54.93 ± 17.37 years. From all, 58.7% (n = 529) were men and 9.9% (n=83) death occurrences were recorded. Sixty-five years of age was estimated as the effective change- point that could change the cure rate of patients at the end of the follow-up times. The cure rate at any time during 30 and 180 follow-up days was noticeably higher in COVID-19 patients younger than 65 years who had cancer.

Hodgkin lymphoma (HL) is one of the best curable cancers. Many researches have validated the benefit of hematopoietic stem cell transplant (HSCT) for patients with relapsed or primary resistant HL. This analysis aimed to identify an effective change point in patients' age, the cure fraction before and after the change point, and significant prognostic factors on the cure fraction before and after the change point for these patients after HSCT in Iran.

In this retrospective cohort study, there were 156 patients with HL who underwent HSCT from 2007 to 2014 with 18 months of follow up in Tehran, Iran. The survival time was set as the time interval between transplantation and the recurrence of HL. Also, the change point and the cure fraction before and after the change point were estimated using the Bayesian estimation method and log-normal distribution.

The estimated cure fraction was 79.2% for all patients. In susceptible cases, the mean survival time was 999 days (2.7 years). Also, the three and five-year survival rates were 82.1% and 80.0%, respectively. The effective change point in the age at transplantation of patients was 35 years, and the cure fraction before the change point was 84.5 % and after the change point was 60.6%.

The study concluded that the age of 35 years is a significant change point in the age at transplantation. If individuals underwent HSCT with HL before the age of 35, they have a higher survival rate (recurrent of HL) than those underwent HSCT after 35.

There are numerous sophisticated studies which have investigated risk factors of breast cancer (BC). The purpose of this paper is to use benefits of Bayesian modeling to involve such prior information in determining factors affecting the survival of women with BC in Yazd city.

The checklist included the characteristics of the patients and the factors studied. Then, from the records of patients referred to Radiotherapy Center of Shahid Ramezanzadeh, who had BC, from April 2005 to March 2012, the survival of 538 persons was recorded in the census. Data were analyzed by R software version 3.4.2, and 0.05 was considered the significance level.

The mean age of BC diagnosis was 48.03 ± 11016 years. The Bayesian Cox regression showed that surgery (hazard ratio [HR] =1.631 95% PI; 1.102–2.422), ki67 (HR = 3.260. 95% PI; 1.6308–6.372), stage (HR = 5.620, 95% PI; 4.079–7.731), lymph node (HR = 1.765, 95% PI; 1.127–2.790), and ER (HR = 2. 600 95% PI; 2.023–3.354) were significantly related to survival time.

The parametric and cox models were compared with standard error, and Cox model was selected as an optimal model. Accordingly, stage, ki67, lymph node, ER, and surgery variables had a positive effect on death hazard.

There are numerous sophisticated studies which have investigated risk factors of breast cancer (BC). The purpose of this paper is to use benefits of Bayesian modeling to involve such prior information in determining factors affecting the survival of women with BC in Yazd city.

The checklist included the characteristics of the patients and the factors studied. Then, from the records of patients referred to Radiotherapy Center of Shahid Ramezanzadeh, who had BC, from April 2005 to March 2012, the survival of 538 persons was recorded in the census. Data were analyzed by R software version 3.4.2, and 0.05 was considered the significance level.

The mean age of BC diagnosis was 48.03 ± 11016 years. The Bayesian Cox regression showed that surgery (hazard ratio [HR] =1.631 95% PI; 1.102–2.422), ki67 (HR = 3.260. 95% PI; 1.6308–6.372), stage (HR = 5.620, 95% PI; 4.079–7.731), lymph node (HR = 1.765, 95% PI; 1.127–2.790), and ER (HR = 2. 600 95% PI; 2.023–3.354) were significantly related to survival time.

The parametric and cox models were compared with standard error, and Cox model was selected as an optimal model. Accordingly, stage, ki67, lymph node, ER, and surgery variables had a positive effect on death hazard.

This study aims to illustrate the problem of (Quasi) Complete Separation in the sparse data pattern occurring medical data. We presented the failure of traditional methods and then provided an overview of popular remedial approaches to reduce bias through vivid examples. Penalized maximum likelihood estimation and Bayesian methods are some remedial tools introduced to reduce bias. Data from the Tehran Thyroid and Pregnancy Study, a two-phase cohort study conducted from September 2013 through February 2016, was applied for illustration. The bias reduction of the estimate showed how sufficient these methods are compared to the traditional method. Extremely large measures of association such as the Risk ratios along with an extraordinarily wide range of confidence interval proved the traditional estimation methods futile in case of sparse data while it is still widely applying and reporting. In this review paper, we introduce some advanced methods such as data augmentation to provide unbiased estimations.

Different patterns of gender-based relationships between attitude toward smoking and self-esteem with smoking behavior have reported. However, such associations may be much more complex than a simply supposed linear relationship. We aimed to propose a method of providing hand details on the total and gender-based scenarios of the relationships between attitude toward smoking and self-esteem with smoking.
A secondary analysis conducted on a data set obtained for a cross-sectional study among 4,905 male and female high school students in Tabriz, Iran (2012). We randomly selected 196 classes in a clustering process and invited all the students in the classes to participate in the study; then, investigated the relationship between smoking with attitude and self-esteem, as explanatory variables. We also found the data to fit a nonlinear functional relationship and to be free from normal condition due to applying Bayesian nonparametric functional latent variable model.
Among all the students, attitude was found as the only variable with conceptual effect on smoking (p The relationships between self-esteem and attitude with smoking may not be necessarily linear. Being at the high levels of self-esteem among boys may lead to the reduced levels of smoking, and among girls may result in the increased level of the behavior.

Aim: The aim of this study was to obtain more accurate estimates of the liver cancer incidence rate after correcting for misclassification error in cancer registry across Iranian provinces.
Nowadays having a thorough knowledge of geographic distribution of disease incidence has become essential for identifying the influential factors on cancer incidence.
Data of liver cancer incidence was extracted from Iranian annual of national cancer registration report 2008. Expected coverage of cancer cases for each province was calculated. Patients of each province that had covered fewer cancer cases than 100% of its expectation, were supposed to be registered at an adjacent province which had observed more cancer cases than 100% of its expected coverage. For estimating the rate of misclassification in registering cancer incidence, a Bayesian method was implemented. Beta distribution was considered for misclassified parameter since its expectation converges to the misclassification rate. Parameters of beta distribution were selected based on the expected coverage of cancer cases in each province. After obtaining the misclassification rate, the incidence rates were re-estimated.
There was misclassification error in registering new cancer cases across the provinces of Iran. Provinces with more medical facilities such as Tehran which is the capital of the country, Mazandaran in north of the Iran, East Azerbaijan in north-west, Razavi Khorasan in north-east, Isfahan in central part, and Fars and Khozestan in south of Iran had significantly higher rates of liver cancer than their neighboring provinces. On the other hand, their neighboring provinces with low medical facilities such as Ardebil, West Azerbaijan, Golestan, South and north Khorasans, Qazvin, Markazi, Arak, Sistan & balouchestan, Kigilouye & boyerahmad, Bushehr, Ilam and Hormozgan, had observed fewer cancer cases than their expectation.
Accounting and correcting the regional misclassification are necessary for identifying high risk areas of the country and effective policy making to cope with cancer.

Aim: To estimate the change in years of life lost (YLL) due to gastric cancer mortality after correcting for misclassification in registering causes of death using the Bayesian method.
For evaluating the health status of a country and determining priority of risk factors, some epidemiologic indicators are needed. Due to premature death, YLL is one of the most widely used indicators. To have an exact estimate of YLL, an accurate death registry data is needed, but the Iranian death registry is subject to misclassification error.
Material and Gastric cancer mortality data from 2006 to 2010 for Iran were extracted from national death statistics. The rate of misclassification in registered causes of deaths was estimated, using Bayesian method for each year. Then YLL of gastric cancer is estimated for different age-sex categories before and after implementing Bayesian method.
Using Bayesian method, the estimated misclassification rate for gastric cancer in cancer without label group were 5%, 3%, 3%, 7% and 7% respectively from 2006 to 2010. Estimated Years of life lost due to gastric cancer before correcting misclassification were respectively 111684.93, 114957.31, 112391.93, 112250.53 and 113300.92 person-years for years 2006 to 2010. After correcting misclassification, the total YLL of gastric cancer increased to 1535.19, 921.11, 908.39, 2566.39 and 2507.00 person-years, respectively from 2006 to 2010.
If health policy makers ignore the existence of misclassification in registered causes of death, they may underestimate the burden of some causes of death and overestimate some others.

Time to donating blood plays a major role in a regular donor to becoming continues one. The aim of this study was to determine the effective factors on the interval between the blood donations.
In a longitudinal study in 2008, 864 samples of first-time donors in Shahrekord Blood Transfusion Center, capital city of Chaharmahal and Bakhtiari Province, Iran were selected by a systematic sampling and were followed up for five years. Among these samples, a subset of 424 donors who had at least two successful blood donations were chosen for this study and the time intervals between their donations were measured as response variable. Sex, body weight, age, marital status, education, stay and job were recorded as independent variables. Data analysis was performed based on log-normal hazard model with gamma correlated frailty. In this model, the frailties are sum of two independent components assumed a gamma distribution. The analysis was done via Bayesian approach using Markov Chain Monte Carlo algorithm by OpenBUGS. Convergence was checked via Gelman-Rubin criteria using BOA program in R.
Age, job and education were significant on chance to donate blood (P Due to the significance effect of some variables in the log-normal correlated frailty model, it is necessary to plan educational and cultural program to encourage the people with longer inter-donation intervals to donate more frequently.

Nowadays, one of the major public health problems among children is growth failure. It can be characterized in terms of either inadequate growth or the inability to maintain growth..
The main objective of this study was to examine the effects of some factors on growth failure among a sample of infants less than two years old..
The present longitudinal archival study relied on data gathered from health files from February 2007 to July 2010 for 1,358 children under two years of age, selected from eight health centers in the east and northeast parts of Tehran, Iran. In the present study, growth failure refers to at least a 50 g decrease in an infant’s weight as recorded at each attendance in comparison to the previous measurement. The impacts of risk indicators were assessed using the Bayesian hierarchical logistic regression modeling technique..
The highest and lowest percentage of growth failure was 5.8% and 0.1%, respectively, in the eleventh and the first month after birth. The obtained results from the Bayesian hierarchical modeling revealed that diarrhea (95% credible interval (CrI): 0.70 - 3.31), discontinuation of breastfeeding (95% CrI: 0.77 - 5.96), and respiratory infections (95% CrI: 2.07 - 4.61) were significant risk factors for growth failure. The random term at the child level was significant (95% CrI: 0.74 - 7.82), while the variation in centers was extremely small (95% CrI: 0.004 - 4.22)..
It was noted that a relatively high prevalence of growth failure was observed in the study sample. For minimizing the impact of significant risk factors on growth failure, the early detection of growth failure and its risk indicators is of great importance. In addition, when the focus of the analysis is on the different nested sources of variability and the data has a hierarchical structure, using a hierarchical modeling approach is recommended to achieve more accurate results..