Missing data occurs in almost all research and handling it is one of the most difficult tasks when preparing data. If treated inappropriately, missing data may lead to biased estimates and may reduce the predictability of the model. In this video, you will learn about different ways to use in Monolix datasets with missing information and what are the most common methods for handling missing data.