Analyzing patterns of missing data to determine if they correlate with observed variables. Using statistical tests like Little's MCAR (Missing Completely at Random) test to evaluate if the data is MAR or if other missing data mechanisms are at play. Employing sensitivity analyses to understand the impact of missing data on study outcomes.
