Marginal structural models are used to figure out how interventions affect outcomes in non-randomized studies. When data on important factors is missing, it can mess up the results. Different methods are used to deal with missing data, but it's not clear which ones work best. By looking at different ways to handle missing data and doing simulations, researchers found that some methods, like multiple imputation and inverse-probability-of-missingness weighting, are better at reducing bias in the results. It's important to think about why data is missing and how it might affect the relationships between the data you do have when choosing a method to handle missing data in these models.