The MIANALYZE Procedure

Overview

The experimental MIANALYZE procedure combines the results of the analyses of imputations and generates valid statistical inferences. Multiple imputation provides a useful strategy for analyzing data sets with missing values. Instead of filling in a single value for each missing value, Rubin's (1976; 1987) multiple imputation strategy replaces each missing value with a set of plausible values that represent the uncertainty about the right value to impute. You can implement the strategy with two SAS procedures: PROC MI, which generates imputed data sets, and PROC MIANALYZE, which combines the results of analyses carried out on the data sets. These two procedures are available in experimental form in Release 8.2 of the SAS System.

These analyses of imputations are obtained by using standard SAS procedures (such as PROC REG) for complete data. No matter which complete-data analysis is used, the process of combining results from different imputed data sets is essentially the same. This results in valid statistical inferences that properly reflect the uncertainty due to missing values.

The MIANALYZE procedure reads the parameter estimates and associated covariance matrix that are computed by the standard statistical procedure for each imputed data set. The MIANALYZE procedure then derives valid univariate and multivariate inferences for these parameters.

For some parameters of interest, it is not straightforward to compute estimates and associated covariance matrices with standard statistical SAS procedures. Examples include correlation coefficients between two variables and ratios of variable means. Special cases such as these are described in the "Examples of the Complete-Data Inferences" section.

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