In epidemiological research, missing data can introduce bias and reduce the precision of estimates. Traditional methods like complete case analysis or simple imputation can lead to incorrect conclusions. PMMs, however, incorporate the missing data mechanism into the analysis, allowing for more accurate and unbiased results. By modeling the data conditional on the missingness patterns, PMMs help to mitigate the impact of missing data on study findings.
