Why is Multiple Imputation Important in Epidemiology?
Missing data is a common issue in epidemiology studies, often due to non-response, dropouts, or incomplete records. Ignoring missing data or using naive methods to handle it can lead to biased results and reduced statistical power. Multiple imputation helps to mitigate these issues, providing improved accuracy and reliability in epidemiological analyses.
