Background & Aim: One of the basic assumptions in simple linear regression models is the statistical independence of observations. Sometimes this assumption is not true for study subject and consequently the use of general regression models may not be appropriate. In this case, one of the leading methods is the use of multilevel models. The present study utilizesmultivariate logistic regression model using a multilevel  model to exhibit the chance of having elbow, wrist and knee disorders over the past year based on elbow, wrist and disorders during the past week.
Methods & Materials: This study is a cross-sectional study that was carried out from April 2015 to May 2016 in Mobarakeh Steel Company, Isfahan. The study population includes 300 male employees of Mobarakeh Steel Company, with a mean age of 41.40±8.17 years and an average working experience of 16.0±7.66 years. Data were analyzed using SPSS (version 24) and MLwiN software.
Results: Based on this study, results obtained from single variable and multivariable regression were different.
Conclusion: Based on this study, it can be suggested that multivariable regression cause a better and more accurate deduction compared to single variable method.

Background & Aim: Awareness of adolescents’ general health status is important. The aim of this study was an assessment of general health status by estimating the mean of general health score of high school students in eight counties of Bushehr province in 2014 with small area method.
Methods & Materials: In this cross-sectional study, students’ general health score at the counties levels was estimated by small area method. The data were collected based on the general health questionnaire  GHQ28) and the accuracy of estimations was measured by using of standard error.
Results: The overall estimated mean±standard error of general health score for 138 students, using the synthetic estimation of small area method was 26.65±0.21. The means of general health at eight counties were 26.96, 26.85, 26.41, 26.48, 26.74, 26.76, 26.62 and 27.41, respectively
Conclusion: A little difference between the averages and the small standard errors of estimations in eight counties, showed that the synthetic estimator has a high accuracy for estimating the mean of adolescents’ general health scores.

Background & Aim: Due to the applicability of the statistical distributions in many areas of sciences, adding parameters to an existing distribution for developing more flexible models have been overlooked in the statistical literatures.
Methods & Materials: A new generalization of power distribution is proposed using alpha power transformation method. The new distribution is more flexible than the power distribution and contains distributions that can be unimodal or right skewed.
Results: We study some statistical properties of the new distribution, including mean residual lifetime, quantiles, mode, moments, moment generating function, order statistics, some entropies and maximum likelihood estimators.
Conclusion: We fit the APP and some competitive models to one real data set and show that the new model has a superior performance among the compared distributions as evidenced by some goodness-of fit statistics.

Background & Aim: Mixed Poisson and mixed negative binomial distributions have been considered as alternatives for fitting count data with over-dispersion. This study introduces a new discrete distribution which is a weighted version of Poisson-Lindley distribution.
Methods & Materials: The weighted distribution is obtained using the negative binomial weight function and can be fitted to count data with over-dispersion. The p.m.f., p.g.f. and simulation procedure of the new weighted distribution, namely weighted negative binomial- Poisson-Lindley (WNBPL), are provided. The maximum likelihood method for parameters estimation is also presented.
Results: The WNBPL distribution is fitted to several datasets, related to genetics and compared with the Poison distribution. The goodness of fit test shows that the WNBPL can be a useful tool for modeling genetics datasets.
Conclusion: This paper introduces a new weighted Poisson-Lindley distribution which is obtained using negative binomial weight function and can be used for fitting over-dispersed count data. The p.m.f., p.g.f. and simulation procedure are provided for the new weighted distribution, namely the weighted negative binomial-Poisson Lindley (WNBPL) to better inform parents from possible time of occurrence reflux and treatment strategies.

Background & Aim: Over the past decades, due to the high acceptance of patients in hospitals, the consequent relapse of the disease, the high cost of treatment and medication, the lack of coordination between the provision of hospital services and the needs of the community, the necessity of paying attention to the status of special patients and conducting a study for designing a suitable model in the country has been increased.
Methods & Materials: This research is descriptive-correlation-exploratory and the data were collected through qualitative and quantitative methods. The research sample involves 392 persons in the quantitative section and 20 in the qualitative section. The research instrument was an open-ended questionnaire and a test; the data were collected using cluster sampling.
Results: A total of 7 factors were identified as the final model of health management services for special patients, including: policymaking and load factor planning (0.72), organizational structure (0.63), government intervention methods in financing (0.81), control mechanism (0.88), government intervention in the affairs of special patients (0.79), pharmaceutical and therapeutic support services and medical aids (0.69), and educational support and prevention services (0.68). Also, the fitting of the designed model is 0.027, indicating appropriate fit for the model.
Conclusion: Providing health services to patients by government has a significant role in the patient health services management, and prevention-based education, identification of primary risk factors, and preventing the epidemics and applying the pattern of health services management to special patients with all dimensions and emphasizing the effective role of government control over the provision of services can lead to presenting executive strategies over time.

Background & Aim- Medical tourism industry would not be developed without well-known healthcare centers which popular for their therapeutic outcome, efficiency, patient centeredness, responsive to governance and staff orientation. To do so, this study designed and carried out.
Materials & Methods- This was an applied, correlation and cross-sectional study which was done in first quarter of 2018. The population of study consisted of International patient department hospitals staff that were selected randomly (N=263). There were used two kinds of research-made questionnaires for medical tourism development and hospital performance assessment which were included of 14 and 45 questions respectively and validated by experts. The reliabilities determined as 0.85 for medical tourism development and 0.68 for hospital performance assessment by Cronbach’s alpha coefficient .The Pearson correlations counted as 0.85. SPSS version 21 was used for all descriptive and inferential analysis.
Results- It has been shown that there was a significant correlation between all hospital performance assessment dimensions and medical tourism industry (P<0.05). The most correlation related to patient centeredness (r=0.45) and the least went to staff orientation (r=0.30). Total amount of correlation between two variables determined as 0.76.
Conclusion- There was a significant and direct relation between hospital performance assessment dimensions and medical tourism development. So, it is strongly recommended that hospital managers as well as policy makers implement hospital performance strengthening strategies for generating medical tourism attraction.

Background & Aim: The bootstrap is a method that resample from the original data set. There are the wide ranges of bootstrap application for estimating the prediction error rate. We compare some bootstrap methods for estimating prediction error in classification and choose the best method for the microarray leukemia classification.
Methods & Materials: The sample consist of n=38 patients with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) with p=4120 genes that n<<p from an existing database. We carried out following steps. (1) Resample from the original sample. (2) Divide the sample to two sets, learning set and test set by 3-fold cross validation. (3) Train 1NN, CART and DLDA classifiers and compute its misclassification error by comparing the predicted class of the remaining samples with the true class. (4) Average the
errors on B bootstrap samples.
Results: Standard deviation, bias and MSE for comparing four bootstrap methods by three classifiers were computed. For choosing the best method, we assess a bias-variance tradeoff in the behavior of prediction error estimates. The 0.632+ BT is approximately un-bias and has small variability. However, the LOOBT procedure has big variability and is biased. Also we provide a table and some figures in the section 4.
Conclusion: The bias and variance of the prediction error rates have high variability in various bootstrap methods. Although the 0.632+ BT is approximately un-bias and has small variability, other resampling methods maybe are useful for the microarray classification in the different situations.

Background & Aim: The sample standard deviation S is the common point estimator of σ, but S is sensitive to the presence of outliers and may not be an efficient estimator of σ in skewed and leptokurtic distributions. Although S has good efficiency in platykurtic and moderately leptokurtic distributions, its classical inferential methods may perform poorly in non-normal distributions. The classical confidence interval for σ relies on the assumption of normality of the distribution. In this paper, a performance comparison of six confidence interval estimates of σ is performed under ten distributions that vary in skewness and kurtosis.
Methods and Material: A Monte Carlo simulation study is conducted under the following distributions: normal, two contaminated normal, t, Gamma, Uniform, Beta, Laplace, exponential and χ2 with specific parameters. Confidence interval estimates obtained using the more powerful ranked set sampling (RSS) are compared with the traditional simple random sampling (SRS) technique. Performance of the confidence intervals is assessed based on width and coverage probabilities. A real data example representing birth weight of 189 newborns is used for assessment.
Results: It is not surprising that for normal data most of the intervals were close tot he nominal value especially using RSS. Simulation results indicated generally better performance of RSS in terms of coverage probability and smaller interval width as sample size increases, especially for contaminated and heavy-tailed skewed distributions.
Conclusion: Simulation results revealed that the use of RSS improved greatly the coverage probability. Also, it was found that the interval labeled (III) due to Bonett (2006) had the best performance in terms of coverage probability over the wide range of distributions investigated in this paper and would be recommended for use by practitioners. There may be a need to develop nonparametric intervals that is robust against outliers and heavy-tailed distributions.

Background& Aim: Given the high rate of smoking around the world and in our country and prevalence of smoking among people, the family groups and youth at fertility age and from other side due to toxic substances in cigarette smoke and its effect on smokers and their surroundings, one should take substantial action about this hygiene problem. Apart that damaging himself and society, the smoker mars his surrounding environment, and as expert believe, the smoker not only extirpate his own life but also contribute to blot out the families.
Methods & Materials: In this review study, firstly the search of registered papers in electronic sites have been examined, and the keywords related to this title has been used and 9 papers are assessed.
Results: Findings suggest the positive effect of counselling of family based on BASNEF model on smoking of smokers living with pregnant women.
Conclusion: One of the models used for change of people behavior is BASNEF model that is a complete and comprehensive model for behavior study and change. BASNEFF model considers the factors like beliefs, attitudes, social norms and enablers as effective for change behavior.