Pattern Mixture Models (PMM) are typically used to handle nonignorable missingness. They factorise the joint likelihood of measurement process and missingness process into a marginal density of the missingness process and the density of the measurement process conditional on the missing data patterns, where the model of interest is fitted for each pattern.