Missing data is a common issue in epidemiological studies. Techniques to handle missing data include:
Imputation: Filling in missing values with estimated ones. Sensitivity Analysis: Assessing how results change with different assumptions about the missing data. Multiple Imputation: Creating several complete datasets by imputing missing values multiple times and combining the results.
