Iranian Journal of

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Background and Objectives: Missing data exist in many studies, e.g. in regression models, and they decrease the model's efficacy. Many methods have been suggested for handling incomplete data: they have generally focused on missing outcome values. But covariate values can also be missing.
Materials and Methods: In this paper we study the missing imputation by the EM algorithm and auxiliary variable and compare the result with case-complete analysis in a logistic regression model dealing with factors that influence the choice of the delivery method.
Our data came from a cross-sectional study of factors associated with the choice of the delivery method in pregnant women. The sample size in this cross-sectional study was 365 and the data were collected through interviews, using questionnaires covering several demographic variables, delivery history, attitude, and some social factors. We used standard deviations to compare the efficiency of the two methods.
Results: The results show that maximum likelihood analysis by EM algorithm is more effective than case-complete analysis.
The problem of missing data is common in surveys and it causes bias and decreased model efficacy. Here we show that the EM algorithm for imputation in logistic regression with missing values for a discrete covariate is more effective than case-complete analysis.
Conclusion: On the other hand if missing values occur for a continuous covariate then we have to use other methods or change the variable into a discrete one.

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Background & Objectives: Cancer has been traditionally regarded as a fatal disease it is a major public health problem in many countries throughout the world. In recent years, cancer morbidity and mortality has increased in our country and notably stomach cancer now ranks second or third among all cancers types with regard to morbidity.
Methods: Our study included all gastric cancer patients registered in the cancer registry of Fars province. The patients' survival status was followed using phone calls and death records from hospitals, other medical centers, and the city's cemetery. Data analysis involved the use of the nonparametric Kaplan-Meier and Cox proportional hazards models and was performed with the software package SPSS V.13.
Results: Of the 442 patients with gastric cancer, 303 cases (68.6 percents) were male, and the mean age of patients was 58.41 years (SD=14.46). In univariate analysis with the KM method, a statistically significant association was found between survival rates and the following factors: age at diagnosis (P<0.001), tumor grade (P=0.009), presence of metastases (P<0.001), and type of the initial treatment (P=<0.001). Factors without a significant relationship with the survival rate included sex, ethnicity, weight, BMI, tobacco use, history of cancer in close or distant relatives, place of residence, number of children, marital status, occupation, and income. In Cox regression, only age at diagnosis, tumor grade, and the presence of metastases showed a significant association with survival rates.
Conclusions: Our results imply that early detection of cancer at a lower age and in lower tumor grades could be important for increasing the patients' life expectancy.

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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.

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Background & Objectives: Hypertension is one of the most prevalent and important risk factor of cardio-vascular diseases. The aim of this research was to determine relative factors on hypertension in Kohrang.
 Methods: This survey was a population – based case - control study. The study population consisted of 415 patient with hypertension (cases) and 415 controls without any history of cardiovascular and or cerebrovascular diseases & hypertension. A systematic random sampling was used. The chi-square test and conditional logistic regression model was used and the data were analyzed by STATA.
Results: Family history of hypertension, age over 60, no physical activity, bmi≥30 were calculated as risk factors with odds ratio: 2.33 (95% CI 1.58-3.47), 2.01(95% CI 1.24-2.67), 1.8 (95% CI 1.2-2.7), 1.66 (95% CI 1.32-2.07) respectively (p<0.05). Fish consumption, unsaturated fat consumption and literacy were considered as protective factors with an odds ratio: 0.516 (95% CI 0.35-0.69), 0.514 (95% CI 0.36-0.72), 0.28 (95% CI 0.17-0.45) respectively (p<0.01).
Conclusions: The findings of this study highlight to plan appropriate health promotion programmes by health policy makers.

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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.

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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.

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Background & Objective: Using parametric models is common approach in survival analysis. In the recent years, artificial neural network (ANN) models have increasingly used in survival prediction. The aim of this study was to predict of survival rate of patients with gastric cancer by using a parametric regression and ANN models and compare these methods.
Methods: We used the data of 436 gastric cancer patients from a cancer registry in Tehran between 2002-2007. All patients had a confirmed diagnosis. Data were randomly divided into two groups: training and testing (or validation) set. For analysis of data we used a parametric model (exponential, Weibull, normal, lognormal, logistic and log-logistic models) and a three layer ANN model. In order to compare of the prediction of two models, we used the area under receiver operating characteristic (AUROC) curve, classification table and concordance index.
Results: The prediction accuracy of the ANN and the parametric (Weibull) models were 79.45% and 73.97% respectively. The AUROC for the ANN and the Weibull models were 0.815 and 0.748 respectively.
Conclusions: The ANN had a better predictions than the Weibull model. Thus it is suggested to use of the ANN model survival prediction in field of cancer.

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Background & Objectives: Proceed model is widely used in health promoting program. The aim of this study was to investigate the effect of PRECEDE Model –based educational program on nutritional behaviors in a rural society.
Methods: This study was a quasi-experimental (before – after) study and conducted on one hundred eighteen women in eight rural areas in Fridan. Based on precede model, CHD mortality rate, incorrect nutritional habits and nutritional behaviors were identified as the most important indicators. During educational intervention, predisposing factors, enabling factors and reinforcement factors were noticed.The training content was designed based on precede model contains 3 educational sessions weekly for 2 months.
Results: Following the educational intervention, the mean score of knowledge, attitude were significantly increased and predisposing, Enabling and Reinforcing factors as well as behavior were improved. Approximately nine percent of participants perceived they are at risk of heart diseases and 12.96% believed that their regimes are not healthy (bad). Near 48% indicated that heart diseases are preventable diseases.
Conclusion: The finding of current study confirmed the practicability and effectiveness of the PRECEDE Model –based educational program on Behavioral.

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Background & Objective: Mixed outcomes arise when, in a multivariate model, response variables measured on different scales such as binary and continuous. Artificial neural networks (ANN) can be used for modeling in situations where classic models have restricted application when some of their assumptions are not met. In this paper, we propose a method based on ANNs for modeling mixed binary and continuous outcomes.
Methods: Univariate and bivariate models were evaluated based on two different sets of simulated data. The scaled conjugate gradient (SCG) algorithm was used for optimization. To end the algorithm and finding optimum number of iteration and learning coefficient, mean squared error (MSE) was computed. Predictive accuracy rate criterion was employed for selection of appropriate model. We also used our model in medical data for joint prediction of metabolic syndrome (binary) and HOMA-IR (continues) in Tehran Lipid and Glucose Study (TLGS). The codes were written in R 2.9.0 and MATLAB 7.6.
Results: The predictive accuracy for univariate and bivariate models based on simulated dataset Ι, where two outcomes associated with a common covariate, were shown to be approximately similar. However, in simulated dataset ΙΙ in which two outcomes associated with different covariates, predictive accuracy in bivariate models were seen to be larger than that of univariate models.
Conclusions: It is indicated that the predictive accuracy gain is higher in bivariate model, when the outcomes share a different set of covariates with higher level of correlation between the outcomes.

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Background and Objectives: Each year almost 400,000 people are diagnosed with oesophageal cancer worldwide. Wide variation in incidence has been reported both between countries and in different ethnic groups and populations within a country. The area with the highest reported incidence for oesophageal cancer is the so-called Asian ‘oesophageal cancer belt’, which stretches from eastern Turkey through north-eastern Iran, northern Afghanistan and southern Russia to northern China. In the high risk area of Gonbad in Iran, world age-standardised rates are more than 200 per 100,000 and the male/female ratio is reported as 0.8:1.0.This study aimed to assess the risk factors and demographic factors influencing survival of patients with esophageal cancer in north of Iran using weibull and log-logistic regression models.
 Methods: Demographic and clinical data of 359 patients with confirmed diagnosis of esophageal cancer from Babol Cancer registry utilized for our model. parametric and weibull models were employed to analyze the data. The Akaike information criterion (AIC) was also considered as a criterion to select the best model(s). All p values as 0.05 were considered as statistically significant.
Results: The sample study consisted of 62.7% men and 37.3% women. Estimated survival rates in 1, 3, and 5 years following diagnosis were 23%, 15%, and 13% respectively. According to AIC criterion, the hazard rate of non-monotonic and rejection proportional hazards assumption (p<0.05), log-logistic model was more efficient than weibull model. Family history of having cancer in patients showed a significant difference in both models.
Conclusion: It is concluded that early detection of people with a family history of cancer can be effective as an important factor in reducing the risk of death in patients with esophageal cancer.

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Background & Objectives: Osteoporosis is major public health concern affecting millions of adults particularly older adults and women worldwide. Designing effective educational intervention is principle in any health promotion program. The purpose of this study was to evaluate the efficacy of an educational intervention based on health belief model on knowledge about, attitudes toward and practice of prevention osteoporosis among women with low socioeconomic status in Isfahan.
Methods: The study population consisted of 14 women with low socioeconomic status and under 60 years old. A valid and reliable questionnaire developed and used as measurement tool for initial and final assessments in this program. In addition calcium intake and vitamin D, physical activity and exposure to the sun were assessed.
Results: The mean age of the participants were 40.8 ± 10.52 years. The mean score of all parts of health belief model (except for perceived barriers), knowledge, sun exposure, and physical activity after educational intervention compared to before intervention, were increased significantly. There was no statistically significant difference between daily calcium and vitamin D intake before and after intervention.
Conclusion: It is concluded that the HBM Model– based educational program on Knowledge and belief regarding Osteoporosis prevention seems practical and effective. However more research should be done to find out more effective intervention regarding optimal calcium and vitamin D intake.

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Normal 0 false false false EN-US X-NONE AR-SA The relationship between two things if one is another originator or creator, called causality. Although this concept is not specified to Medical Sciences and Epidemiology, the importance of this issue is more highlighted in the field of epidemiology. Causation is the most basic concepts in empirical sciences and is still under discussion because it is dependent on the basis of any scientific laws without acceptance something cease causality is impossible. With the increasing development of science as well as epidemiology, causality has found a broader concept and its application in analytical studies and logical interpretation of the results of this type of study, has a wider dimension. Due to developing new epidemiology courses at medical universities and increase the number of students, it is felt to talk more about the causality concept. In this review causality concepts in the humanities is overviewed, its history is briefly described, the causality of Medical Epidemiology and also Islamic religion is considered, then the causality framework, and models to interpret the conventional causality will be discussed.

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Background & Objectives: Peritoneal dialysis is one of the most common types of dialysis in patients with renal failure. However multivariate analysis such as log- rank test and Cox have usually used to evaluate association of risk factors in survival of this group of patients, the aim of this study was to perform of Weibull, Gamma, Lognormal and Logistic Mixture cure models in survival analysis of these patients.
Methods: Data of 433 patients undergoing CAPD who registered in two centers in Tehran, Iran between 1997 to 2009 were used in this analysis. We investigated center, gender, age, cholesterol, Low Density Lipoprotein (LDL), High density lipoprotein (HDL), triglyceride, albumin, hemoglobin, creatinine, Fasting Blood Sugar (FBS), calcium and phosphorous as variables effect with Kaplan-Meier and cure model. CUREREGR module was used for survival analysis.
Results: Comparison of AIC (Akaike Information Criterion) of Weibull, Gama, Lognormal and Logistic Mixture cure models showed that Weibull distribution AIC is lower for almost all variables than other distributions. Weibull distribution has better fitness for data than others. In the multivariate Weibull model, age and albumin variables had significant effect on long-term survival of patients (P<0.01). Triglycerides effect on long-term survival had borderline (P = 0.065). Also HDL, FBS and calcium were significant on short term survival (P<0.01) but significance of LDL was borderline (P=0.088).
Conclusion: Cure models have the ability to analyze dialysis patients' survival data and can differentiate long-term survival from short- term survival. The interpretation of survival data with these statistical models could be more accurate and would help to make better prediction for patients by health care professionals.

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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.

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Background & Objectives: Back pain represents one of the most common occupational problems in nursing. Since the correct posture has a key role in prevention of back pain, this study was performed to determine of operating- room nurses' readiness to adopt correct posture based on Transtheoretical Model (TTM) .
 Methods: This descriptive-analytical study included a convenience sample of 110 operating- room nurses employed at four hospitals in city of Hamadan. Participations completed a designed questionnaire to assess the readiness of change based on TTM.
 Results: The results of present study revealed that by increasing the stage of change (from precontemplation to maintenance), adopting correct posture in the nurses, increased as well (P=.01). Also, by increasing the stage of change, self-efficacy for adopting correct posture increased (P=.03) and perceived cons decreased (P=.02). Stage of change constructs could predict 68% variance of adopting correct posture in the nurses.
 Conclusion: The results of present study indicated that the majority of operating-room nurses are in pre-operational levels (precontemplation, contemplation and preparation) for adopting correct posture. Considering stages of change as an intervening variable may contribute in any future intervention for this group.

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Background & Objectives: Although several studies have been carried out for evaluation of the first birth interval, none of them has considered the presence of infertile women within the sample. Therefore, the aim of this study was to employ survival analysis to study the first birth interval and its determinant factors more accurately.
Methods: In Data from 1068 married women of reproductive age in west Azarbaijan province were considered in this investigation. Two-stage sampling design was used to collect data via a questionnaire, modified Gompertz model, a special kind of cure models, was employed in this study. For descriptive and analytical data analysis, SPSS 16 and R 2.12 were used respectively.
Results: In this study, the average interval between marriage and first birth was 3.9± 0.7 (± SD) years. Using modified Gompertz model, among all demographic factors only mother’s education had significant effect on the first birth interval so that with increasing mother’s educational level, the first birth interval had also increased. (P =0.007). In addition, the estimation of the proportion of women who did not have any children was 0.062 that showed a positive trend with increasing mother’s educational level.
Conclusion: This study revealed that due to the presence of infertility among married women the use of Modified Cured Gompertz model is an appropriate method for evaluation of the first birth intervals and it's determinant factors.

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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.

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Background & Objectives: Previous studies and reports show that majority of motorcyclists do not use helmet in Iran. The aim of study was to evaluate the performance of motorcycle helmet use based on health belief model in Markazi province in Iran.
Methods: This cross- sectional study was carried out on 384 subjects selected based on convenience sampling. Participants completed a designed questionnaire on attitudes and behaviors relevant to bicycle helmet use.
 Results: The mean age of participants was 28.9 ± 8.5 years and 42% of drivers were single. There were significant association between performance with perceived severity, benefits, barriers, and motorcyclists’ action (P<0.05). The barriers of using helmet reported by participants were sweating, hearing disturbance, expensiveness, heaviness, and visual limitation, respectively. In time of study, only 16.2% of motorcyclists used helmet. Based on our health belief model, 61.4% of participants believed that education and information are the best method, 22.7% believed that not users of helmet should pay a penalty, and 15.9% believed that confine of motorcycle could be effective actions to encourage them to use helmet.
 Conclusion: According to the results, the helmet use practice was poor and should be increased Meanwhile, the perceived severity should increase especially in young people. The community - based health education programs accompanied with police harsh treatment is necessary.

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  Background and Objectives : The aim of this study was to assess the disease trajectory and recurrence rate of pemphigus based on the analysis of the gap time between successive recurrent events. In this regard, the most important associated factors with the risk of recurrence could be explained.

  Methods: This longitudinal study was performed on 112 pemphigus patients who attended the dermatology department of Imam Khomeini Hospital, Tehran, Iran, from March 2006 to January 2013. The study duration was considered from the diagnosis of the disease to December 2013. Recurrent events were analyzed based on the gap time between successive events using the multivariate time dependent frailty model. The time between two recurrent gap times was determined monthly between two successive events.

  Results : Decreasing the gap times between two successive events indicates that the subsequent event after the first recurrence occurs with shorter time intervals. So, the disease trajectory represents an increase in the recurrence rate over time. Based on the results of multivariate frailty model, IgG antibody's level was the only effective factor on the recurrence hazard rate of the patients. Also, this model proved that the frailty effects were time dependent frailties.

  Conclusion: Assessing the disease trajectory and recurrence hazard rate can be achieved through analyzing the gap time between successive recurrent events. This analysis also identifies the factors that influence the risk of subsequent recurrent events.

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Background & Objectives: Due to the increasing tendency to measure the quality of life in recent years and the extensive quality of life questionnaires, it is important to determine the appropriate method of analyzing data derived from these studies. The aim of the present study was to introduce ordinal logistic regression models as an appropriate method for analyzing the data of quality of life.

Methods: The data was derived from a cross-sectional study on quality of life survey of 938 students. For data analysis, two binary logistic regression models and ordinal logistic regression models were used and the results of these models were compared.

Results: The results of goodness of fit showed that all three models were fitted well. Based on the ordinal logistic regression models, the three variables out of the explanatory variables were statistically associated with the response while based on the binary logistic regression model, after combining two categories of response variable, only two variables were significant. Therefore, combining the categories of the response variable should be avoided as much as possible because it may lead to data loss due to ignoring some of the response categories.

Conclusion: It is concluded that to analyze quality of life data, due to the nature of the response variable, ordinal logistic regression models are recommended considering the fewer parameter estimates and easier interpretation of the results