PMMs stratify the data based on the pattern of missingness and then model each stratum separately. For instance, in a longitudinal study, the data can be divided into groups based on whether participants completed all follow-ups, missed some, or dropped out entirely. Each group's data are then modeled, and the results are combined to provide overall estimates. This approach helps to account for the fact that the reasons for missing data may be related to the study outcomes.
