BackgroundThe purpose of the study was to determine whether the Persian version of the KIDSCREEN-27 has the optimal number of response category to measure health-related quality of life (HRQoL) in children and adolescents. Moreover, we aimed to determine if all the items contributed adequately to their own domain.FindingsThe Persian version of the KIDSCREEN-27 was completed by 1083 school children and 1070 of their parents. The Rasch partial credit model (PCM) was used to investigate item statistics and ordering of response categories. The PCM showed that no item was misfitting. The PCM also revealed that, successive response categories for all items were located in the expected order except for category 1 in self- and proxy-reports.ConclusionsAlthough Rasch analysis confirms that all the items belong to their own underlying construct, response categories should be reorganized and evaluated in further studies, especially in children with chronic conditions.

Background. Breast cancer which is the most common cause of women cancer death has an increasing incidence and mortality rates in Iran. A proper modeling would correctly detect the factors' effect on breast cancer, which may be the basis of health care planning. Therefore, this study aimed to practically develop two recently introduced statistical models in order to compare them as the survival prediction tools for breast cancer patients. Materials and Methods. For this retrospective cohort study, the 18-year follow-up information of 539 breast cancer patients was analyzed by “Parametric Mixture Cure Model” and “Model-Based Recursive Partitioning.” Furthermore, a simulation study was carried out to compare the performance of mentioned models for different situations. Results. “Model-Based Recursive Partitioning” was able to present a better description of dataset and provided a fine separation of individuals with different risk levels. Additionally the results of simulation study confirmed the superiority of this recursive partitioning for nonlinear model structures. Conclusion. “Model-Based Recursive Partitioning” seems to be a potential instrument for processing complex mixture cure models. Therefore, applying this model is recommended for long-term survival patients.

Background: Fluoride plays an important role in preventing dental caries. Low fluoride concentrations cannot prevent dental caries, but ingestion of very high concentrations of fluoride during enamel development and maturation could lead to fluorosis. Fluoridation of drinking water is the most effective and inexpensive method for preventing caries. The mandated concentration of fluoride incorporated into drinking water should consider the mean temperature of each region.

Background: Breast cancer is the main cause of women cancer mortality. Therefore, precise prediction of patients' risk level is the major concern in therapeutic strategies. Although statistical learning algorithms are high quality risk prediction methods, but usually their better prediction quality leads to more loss of interpretability. Therefore, the aim of this study is to compare 'Model-Based Recursive Partitioning' and 'Random Survival Forest'; whether the partitioning, as the more interpretable learning technique, could be a suitable successor for forests.