Tehran University of Medical Sciences Acta Medica Iranica 0044-6025 58 11 2021 01 01 591 598 Mohammad Hossein Panahi Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran,Iran. Kazem Mohammad Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences,Tehran, Iran Razieh Bidhendi Yarandi Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran Fahimeh Ramezani Tehrani Reproductive Endocrinology Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences,Tehran, Iran 2020 06 23 2020 10 23 This study aims to illustrate the problem of (Quasi) Complete Separation in the sparse data pattern occurring medical data. We presented the failure of traditional methods and then provided an overview of popular remedial approaches to reduce bias through vivid examples. Penalized maximum likelihood estimation and Bayesian methods are some remedial tools introduced to reduce bias. Data from the Tehran Thyroid and Pregnancy Study, a two-phase cohort study conducted from September 2013 through February 2016, was applied for illustration. The bias reduction of the estimate showed how sufficient these methods are compared to the traditional method. Extremely large measures of association such as the Risk ratios along with an extraordinarily wide range of confidence interval proved the traditional estimation methods futile in case of sparse data while it is still widely applying and reporting. In this review paper, we introduce some advanced methods such as data augmentation to provide unbiased estimations. https://acta.tums.ac.ir/index.php/acta/article/view/8564