SAS/STAT Software

MI Procedure

Multiple imputation is a strategy for dealing with data sets with missing values. You replace each missing value with a set of plausible values that represent the uncertainty about the right value to impute. You create multiply imputed data sets, analyze them with standard analyses, and then combine the results. You produce valid statistical inferences that properly reflect the uncertainty due to the missing values.

The MI procedure creates multiple imputed data sets for incomplete p-dimensional multivariate data. It offers three methods for creating the imputed data sets: the regression method, the propensity score method, and the Markov Chain Monte Carlo (MCMC) method. The procedure creates an output data set containing m imputed versions of the original data. In each version, the missing values are replaced with imputed values. For the MCMC method, you can specify whether you want a single chain for all m imputations or a separate chain for each imputation. You can also specify the initial estimates for the MCMC method. After analyzing your imputed data with standard procedures, you use the MIANALYZE procedure to combine the results.

The MI procedure was introduced in Release 8.1 and remains experimental in Release 8.2, with various new options and output displays available. Among others, a new TRANSFORM statement enables you to transform variables before imputation and back-transform these variables before combining inferences and creating output data sets.

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