The MNAR statement imputes missing values by using the pattern-mixture model approach, assuming the missing data are missing not at random (MNAR), which is described in the section Multiple Imputation with Pattern-Mixture Models. By comparing inferential results for these values to results for imputed values that are obtained under the missing at random (MAR) assumption, you can assess the sensitivity of the conclusions to the MAR assumption. The MAR assumption is questionable if it leads to results that are different from the results for the MNAR scenarios.
There are two main options in the MNAR statement, MODEL and ADJUST. You use the MODEL option to specify a subset of observations from which imputation models are to be derived for specified variables. You use the ADJUST option to specify an imputed variable and adjustment parameters (such as shift and scale) for adjusting the imputed variable values for a specified subset of observations.
The MNAR statement is applicable only if it is used along with a MONOTONE statement or an FCS statement. For a detailed explanation of the imputation process for the MNAR statement and how this process is implemented differently using the MONOTONE and FCS statements, see the section Multiple Imputation with Pattern-Mixture Models.