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Missing Value Imputation Procedures

• MI — Performs multiple imputation of missing data
• MIANALYZE — Combines the results of the analyses of imputations and generates valid statistical inferences

Missing Value Imputation

Most SAS statistical procedures exclude observations with any missing variable values from the analysis. These observations are called incomplete cases. Although analyzing only complete cases has the advantage of simplicity, the information contained in the incomplete cases is lost. This approach also ignores possible systematic differences between the complete cases and the incomplete cases, and the resulting inference might not be applicable to the population of all cases, especially with a small number of complete cases.

Another strategy for handling missing data is multiple imputation, which replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. The multiply imputed data sets are then analyzed by using standard procedures for complete data and combining the results from these analyses. No matter which complete-data analysis is used, the process of combining results from different data sets is essentially the same.

Below are highlights of the capabilities of the SAS/STAT procedures that perform missing value imputation:

• regression and propensity scoring for monotone missing patterns
• MCMC method for arbitrary missing patterns
• combine results for statistically valid inferences

Statistics and Operations Research Home Page | SAS/STAT Software