MI Procedure
The MI procedure is a multiple imputation procedure that creates multiply imputed data sets for incomplete pdimensional multivariate data.
It uses methods that incorporate appropriate variability across the m imputations. The imputation method of choice depends on the patterns
of missingness in the data and the type of the imputed variable. The following are highlights of the MI procedure's features:
 creates multiple imputed data sets for incomplete multivariate data
 applies parametric and nonparametric methods for data sets with monotone missing values:
 continuous variables:
 regression method
 predictive mean matching method
 propensity score method
 classification variables:
 logistic regression method
 discriminant function method
 enables you to specify a multivariate imputation that uses fully conditional specification (FCS) methods
 applies a Markov chain Monte Carlo (MCMC) method for data sets with arbitrary missing patterns
 uses the EM algorithm to compute the MLE for (μ,Σ), the
means and covariance matrix, of a multivariate normal distribution from the input data set with missing values

 provides the following transformation methods for data that do not satisfy the assumption of multivariate normality:
 BoxCox
 exponential
 logarithmic
 logit
 power
 performs sensitivity analysis by generating multiple imputations for different scenarios under the assumption that the data are missing not at random
 performs BY group processing, which enables you to obtain separate analyses on grouped observations
 creates a SAS data set that corresponds to any output table
 automatically creates graphs by using ODS Graphics

Once the m complete data sets are analyzed using standard SAS procedures, the MIANALYZE
procedure can be used to generate valid statistical inferences about these parameters by combining
results from the m analyses.
For further details see the SAS/STAT User's Guide:
The MI Procedure
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Examples