PMMs work by categorizing data into different patterns based on the observed missingness. Each pattern represents a different subset of the data, and the model estimates the parameters for each subset separately. These estimates are then combined to produce overall parameter estimates. This approach allows researchers to understand how different patterns of missingness might influence the study outcomes.
