background: multifactorial regression models are frequently used in medicine to estimate survival rate of patients across risk groups. however, their results are not generalisable, if in the development of models assumptions required are not satisfied.  missing data is a common problem in pathology. the aim of this paper is to address the danger of exclusion of cases with missing data, and to h...

background: during the last decades, the number of dentistry units increased significantly across the country. the aim of this study was to assess the efficiency of dental units of iran provinces regarding dental health inputs and outputs using data envelopment analysis approach. methods: in this applied descriptive-analytical study, the study population included all of iran 31 provinces. the o...

Background Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern...