 Missing data can significantly affect the accuracy of research findings, particularly when it comes to randomized control trials. This paper provides a comprehensive review of the various methods available for dealing with missing data, including best-wrestled and worst-best sensitivity analyses, multiple imputation, and full information maximum likelihood. It also outlines the steps that must be taken before, during, and after the analysis process to ensure that any missing data does not lead to biased results. This article was authored by Yanis Christian Jacobsen, Christian Glard, J.R.N. Weticev, and others.