Background: Survival after breast conserving surgery (BCS) vs. modified radical mastectomy (MRM) is a controversial issue. In this study, we want to compare the disease-free survival (DFS) of women who underwent BCS with those treated by MRM.Method: In this historical cohort study, a total of 1097 women who were diagnosed with breast cancer between 2001 and 2007 and received modified MRM or BCS were entered into the study and followed up to March 2017. Kaplan-Meier estimator and extended cox model, and Cox proportional hazards model with propensity score WEIGHTING were implemented to compare overall survival between two groups.Results: A total of 283 women with a maximum follow-up of 11.1 years and age 47.17 ± 11.278 were met the inclusion criteria. The results of the extended cox model did not show any difference between the survival of two groups (P = 0.35). After implementing the Cox model with propensity score WEIGHTING, the inferences remained unchanged (P = 0.67).Conclusion: The patients treated with BCS tend to have the same DFS rate as those who underwent a mastectomy in a randomized controlled trial-like setting using propensity score WEIGHTING.

Introduction: Missing values are frequently seen in data sets of research studiesespecially in medical studies. Therefore, it is essential that the data, especially in medical research should evaluate in terms of the structure of missingness. This study aims to provide new statistical methods for analyzing such data. Methods: Multiple imputation (MI) and INVERSE-PROBABILITY WEIGHTING (IPW)aretwo common methods whichused to deal with missing data. MI method is more effectiveand complexthan IPW. MI requires a model for the joint distribution of the missing data given the observed data. While IPW need only a model for the PROBABILITY that a subject has fulldata. Inefficacy in each of these models may causeto serious bias if missingness in dataset is large. Anothermethod that combines these approaches to give a doubly robust estimator. In addition, using of these methodswill demonstrate in the clinical trial data related to postpartum bleeding. Results: In this article, we examine the performance of IPW/MI relative to MI and IPW alone in terms of bias and efficiency. According to the results of simulation can be said that that IPW/MI have advantages over alternatives. Also results of real data showed that, results of MI/MI doesnot differ with the results of IPW/MIsignificantly. Conclusion: Problem of missing data are in many studies that causes bias and decreasing efficacy inmodel. In this study, after comparing the results of these techniques, it was concludedthat IPW/MI method has better performance than other methods.

One of the traditional methods used for the analysis of survival data is the Cox regression technique. This method calculates the conditional risk ratio. However, when the aim of the study is to estimate the effect of exposure in the total population level, using these conditional methods is not apposite. Furthermore, the hazard ratio has disadvantages of its own such as being non-collapsible, having the risk of structural selection bias and variability in time. Given the limitations, it is recommended to use the marginal hazard ratio, which estimates the average causal effect of exposure in the total population level.This study introduces the INVERSE PROBABILITY TREATMENT WEIGHTING (IPTW) as a method of estimating the marginal causal effect. Finally, to illustrate IPTW method, we used Golestan Cohort Study and estimated the marginal causal effect of smoking on time to death due to the upper gastrointestinal cancer (esophageal-gastric).

In this paper, the two-observational  percentile, percentile and maximum likelihood estimation of the PROBABILITY density function of  INVERSE Weibull random variable are studied. Finally, these estimates are compared using simulation studies and a real data.

INVERSE-distance WEIGHTING method is a simple, easy and understandable interpolation in many branches of earth sciences, and it is embedded in the mining software related to estimation, Efforts to enhance the accuracy and precision of this method can be applied to a wider and reliable interpolation process. In this paper, spatial structure of different elements from analysis of rock samples associated with a porphyry copper deposit is studied using variogram. A criterion based on variogram parameters is suggested for each element to calculate the distance power. In order to validate the method, INVERSE-distance WEIGHTING interpolation of the different elements and different values for the INVERSE-distance power is implemented (common values and calculated power), error percent and root mean square error of interpolation is calculated and analyzed. Interpolation is coded in MATLAB environment and the results for different elements are demonstrated and analyzed. Based on the results, the slope of the linear part spherical variogram is measured of the amount of INVERSE-distance power (a) so that the values for the elements with relatively continuous spatial structure equal common values of a (1, 2 and 3) and INVERSE distance WEIGHTING method is applicable for these elements.

Stock return forecasting is one of the most important question for investing in Stock markets. Because of the effects of policy, economic, etc., we need moderns and intelligent models to forecast the returns.The main idea in this research is classifying the stocks into high and low return groups, for this purpose support vector machine (SVM) was used. To elect the best variables for models we used sequential feature selection and in order to evaluate the accuracy of SVM we do the same forecasting with diagonal quadratic discriminant analysis (DQDA). By using paired t-test, we conclude that models have no significant difference.Equal weighted portfolios were created for each models with and without feature selection also, we used posterior PROBABILITY to weight the portfolio of DQDA with feature selection. The returns were calculated for each portfolio during the years 1388-1391. The simulating results are satisfying and all portfolios’ returns are better than market portfolio.