- Analysis of Variance
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- SAS/STAT Procedures A-Z

The MIANALYZE procedure combines the results of the analyses of imputations and generates valid statistical inferences.

A companion procedure, PROC MI, creates multiply imputed data sets for incomplete multivariate
data. It uses methods that incorporate appropriate variability across the *m* imputations.

The 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 and results in valid statistical inferences that properly reflect the uncertainty due to missing values. These results of analyses are combined in the MIANALYZE procedure to derive valid inferences.

The MIANALYZE procedure reads parameter estimates and associated standard errors or covariance matrix that are computed by the standard statistical procedure for each imputed data set. The MIANALYZE procedure then derives valid univariate inference for these parameters. With an additional assumption about the population between and within imputation covariance matrices, multivariate inference based on Wald tests can also be derived.

For some parameters of interest, you can use TEST statements to test linear hypotheses about the parameters. For others, it is not straightforward to compute estimates and associated covariance matrices with standard statistical SAS procedures, and thus, require special techniques. Examples include correlation coefficients between two variables and ratios of variable means.

For further details see the MIANALYZE Procedure

- Example 80.1: Reading Means and Standard Errors from a DATA= Data Set
- Example 80.2: Reading Means and Covariance Matrices from a DATA= COV Data Set
- Example 80.3: Reading Regression Results from a DATA= EST Data Set
- Example 80.4: Reading Mixed Model Results from PARMS= and COVB= Data Sets
- Example 80.5: Reading Generalized Linear Model Results
- Example 80.6: Reading GLM Results from PARMS= and XPXI= Data Sets
- Example 80.7: Reading Logistic Model Results from a PARMS= Data Set
- Example 80.8: Reading Mixed Model Results with Classification Covariates
- Example 80.9: Reading Nominal Logistic Model Results
- Example 80.10: Using a TEST statement
- Example 80.11: Combining Correlation Coefficients
- Example 80.12: Sensitivity Analysis with Control-Based Pattern Imputation
- Example 80.13: Sensitivity Analysis with the Tipping-Point Approach