Background & Aim: In the survival data with Long-term survivors the event has not occurred for all the patients despite long-term follow-up, so the survival time for a certain percent is censored at the end of the study. Mixture cure model was introduced by Boag, 1949 for reaching a more efficient analysis of this set of data. Because of some disadvantages of this model non-mixture cure model was introduced by Chen, 1999, which became well-known promotion time cure model. This model was based on the latent variable distribution of N. Non mixture cure models has obtained much attention after the introduction of the latent activating Scheme of Cooner, 2007, in recent decades, and diverse distributions have been introduced for latent variable.Methods & Materials: In this article, generalized Poisson-inverse Gaussian distribution (GPIG) will be presented for the latent variable of N, and the novel model which is obtained will be utilized in analyzing long-term survival data caused by skin cancer. To estimate the model parameters with Bayesian approach, numerical methods of Monte Carlo Markov chain will be applied. The comparison drawn between the models is on the basis of deviance information criteria (DIC). The model with the least DIC will be selected as the best model.Results: The introduced model with GPIG, with deviation criterion of 411.775, had best fitness than Poisson and Poisson-inverse Gaussian distribution with deviation criterion of 426.243 and 414.673, respectively.Conclusion: In the analyzing long-term survivors, to overcome high skewness and over dispersion using distributions that consist of parameters to estimate these statistics may improve the fitness of model. Using distributions which are converted to simpler distributions in special occasions, can be applied as a criterion for comparing other models.

BACKGROUND AND OBJECTIVE: Different statistical methods can be used to analyze fertility data. In cases that dependent variable is count, Poisson model is applied. If Poisson model is not applicable in a specific situation, it is better to apply generalized Poisson model and in cases that multilevel variable exists, it is better to use multilevel Poisson model. Our goal in this study is to compare both generalized and multilevel Poisson regression model with Poisson regression model in estimating of coefficient of the effective factors on the number of children.METHODS: This is a cross-sectional study. A sample of 1019 women (15-49 years old) from rural area was selected by stratified sampling. The women were categorized into seven groups and in each group the intended samples were selected equally by systematic sampling. Data were analyzed by Poisson regression model, generalized and multilevel Poisson regression model.FINDINGS: The sample mean and sample variance of the number of children were 4.3 and 8.3, respectively. There was a significant relationship between educational status of the spouses, age of marriage, feeding period, economical status and the interval between the children in generalized and multilevel Poisson regression model.CONCLUSION: According to the results of this study, generalized and multilevel Poisson regression models were more suitable for data analysis and it can estimate coefficient effective of factors on the number of children exactly.

Introduction: Different statistical methods can be used to analyze fertility data. When the response variable is discrete, Poisson model is applied. If the condition does not hold for the Poisson model, its generalized model will be applied.The goal of this study was to compare the efficiency of generalized Poisson regression model with the standard Poisson regression model in estimating the coefficient of effective factors in the number of children.Methods: This is a cross-sectional study carried out on a population of married women with the age range of 15-49 years old in Kashan, Iran. The cluster sampling method was used for data collection. Clusters consisted of the urban blocks determined by the municipality. The total number of 10 clusters each of 30 households was selected according to the health center's framework. The necessary data were then collected through a self-made questionnaire and direct interviews with women under study. Further, the data analysis was performed by using the standard and generalized Poisson regression models through the R software.Results: The average number of children for each woman was 1.45 with a variance of 1.073.A significant relationship was observed between the husband's age, number of unwanted pregnancies, and the average duration of breastfeeding with the present number of children in the two standard and generalized Poisson regression models (p<0.05). The mean age of women participating in this study was 33.1±7.57 years (from 25.53 years to 40.67) and the mean age of marriage was 20.09±3.82 (from16.27 years to23.91), and the mean age of their husbands was 37.9±8.4years (from 29.5 years to 46.3). In the current study, the majority of women were in the age range of 30-35 years old with the median of 32 years old, however, most of men were in the age range of 35-40 years old with the median of 37 years old. While 236 of women did not have unwanted pregnancies, most participants of the present study had one unwanted pregnancy.Conclusion: According to the achieved results, the generalized Poisson regression model is more suitable for data analysis and can estimate the coefficient of effective factors on the number of children more precisely.

Background: Breast cancer is one of the most common diseases in women and causes more deaths rather than other cancers. The increasing trend of breast cancer in Iran makes clear the need of extensive breast cancer research in this area. Some studies showed that in the variety countries and even in the different areas in one country has different risk of breast cancer incidence and this is a reason that there is a correlation between region of life and risk of breast cancer. The purpose of this study was to determine the spatial structure associated with the incidence of breast cancer based on statistical models and identification of areas with high incidence of breast cancer in Iran. Methods: This ecological study was conducted in Kermanshah University of Medical Sciences, Iran, from February to July 2018. Data on breast cancer patients in all provinces of Iran (30 provinces) were investigated since 2004 to 2009. Risk factors in this study included fruit and vegetable consumption, physical activity, overweight or obesity, and human development index. In this study, we have used routine and spatial Poisson's generalized linear mixed models for data analysis. Results: In both routine and spatial models, direct and significant correlation was found between the incidence of breast cancer and the human development index (P<0. 05). In addition to human development index, overweight or obesity factors were also had direct and significant relationship to the incidence of breast cancer in the spatial Poisson's generalized linear mixed model (P<0. 05). In the spatial Poisson's generalized linear mixed model with correlation structure of Besag Yorg Molie (BYM), two provinces of Gilan and East Azerbaijan had the highest risk of breast cancer incidence and province of Kohgiluyeh and Boyer Ahmad had the lowest risk of breast cancer incidence. Conclusion: The results showed that the distribution of breast cancer incidence in Iran has a spatial structure. That is, the adjacent provinces have similar incidences of this disease.

Estimating the final price of products is of great importance. For manufacturing companies proposing a final price is only possible after the design process over. These companies propose an approximate initial price of the required products to the customers for which some of time and money is required. Here using the existing data of already designed transformers and utilizing the bayesian analysis of generalize poisson models and artificial neural networks, a shortcut method for estimating the material and final price of transformers is established. The proposed method being quite precise and fast, without any cost.

Background & objectives: statistical modeling explicates the observed changes in data by means of mathematics equations. In cases that dependent variable is count, Poisson model is applied. If Poisson model is not applicable in a specific situation, it is better to apply the generalized Poisson model. So, our emphasis in this study is to notice the data structure, introducing the generalized Poisson regression model and its application in estimates of effective factors coefficients on the number of children and comparing it with Poisson regression model results.Methods: Besides introducing Poisson regression model, we introduced its application in fertility data analysis. A sample of 1019 women in rural areas of Fars was selected by cross sectional and stratified sampling methods. The number of children of family was determined as a count response variable for model validation.Results: The sample mean and sample variance of the response variable Y, the number of children, are respectively 4.3 and 8.3 (over-dispersion). Log-likelihood was -1950.93 for Poisson regression and -1946.93 for generalized Poisson regression model.Conclusions: The results revealed that this data have over-dispersion. According to selection criteria, the suitable model for this data analysis was generalized Poisson regression model. It can estimate effective factors coefficients on the number of children exactly.

Background & Objective: Due to the benefits of integrated pest management and its environmental approach, this method is now strongly emphasized as one of the components of sustainable agricultural development. The main purpose of this study is to determine the factors affecting the adoption of integrated pest management practices in apple orchards in Urmia. Materials & Methods: In this study, the Poisson regression model and generalized form were used and the required data was collected through a questionnaire from apple farms in Urmia county. For this purpose, using proportional stratified random sampling method, information of 350 apple orchardists was gathered. Results: Estimates of the generalized Poisson model showed that environmental perspectives index, levels of education, income, membership of local cooperatives, apple cultivation area, participation in advanced courses in apple production, knowledge of IPM and the years of IPM adoption has a positive and significant impact on the extension of IPM practices. In contrast, the index of difficulty in the adoption of integrated pest management practices has a negative and significant effect. Conclusion: The results confirm that gardeners pay special attention to agricultural sustainability and environmental protection when conducting IPM projects. Therefore, the Ministry of Jihad-Agriculture can support the development of the sustainability of the local agriculture by raising awareness of integrated pest management practices among farmers and providing targeted training as well as increasing the adoption of this method.