In epidemiology, understanding the pattern and mechanism of missing data is crucial because it affects the validity and reliability of study results. If the missing data are MNAR, then ignoring this aspect can lead to biased estimates and incorrect inferences. For example, in a study on chronic diseases, patients with severe symptoms might be less likely to complete follow-up surveys, leading to an underestimation of the disease's prevalence and severity.
