Specific methods are required to make inference under nonignorable nonresponse assumptions, that is when the value of the variable that is missing is related to some values which are not observed by the analyst (e.g. the missing values themselves)

Selection Models (SM) are typically used to handle nonignorable missingness. They factorise the joint likelihood of measurement process and missingness process into a marginal density of the measurement process and the density of the missingness process conditional on the outcomes, which describes the missing data selection based on the complete data.

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.

Shared Parameter Models (SPM) are typically used to handle nonignorable missingness. In these models a random effect is shared between the repeated measures model and the missing data mechanism model

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