Pattern mixture models (PMMs) are statistical techniques used to analyze data with missing values. Unlike other methods that attempt to impute or ignore missing data, PMMs incorporate the missing data patterns directly into the analysis. This is particularly important in cancer research, where missing data is common due to various reasons such as loss to follow-up, patient dropout, or incomplete data collection.
