Missing Not At Random (MNAR) refers to a scenario in data analysis where the probability of a data point being missing is not random but is related to the unobserved data itself. In other words, the missingness mechanism is associated with the value of the variable that is missing. This poses significant challenges in epidemiological research and data analysis because traditional methods for handling missing data, like complete case analysis or multiple imputation, may produce biased results if the missingness mechanism is not properly accounted for.
