Example 55.4 Reading Mixed Model Results from PARMS= and COVB= Data Sets

This example creates data sets containing parameter estimates and covariance matrices computed by a mixed model analysis for a set of imputed data sets. These estimates are then combined to generate valid statistical inferences about the parameters.

The following PROC MIXED statements generate the fixed-effect parameter estimates and covariance matrix for each imputed data set:

   proc mixed data=outmi;
      model Oxygen= RunTime RunPulse RunTime*RunPulse/solution covb;
      by _Imputation_;
      ods output SolutionF=mixparms CovB=mixcovb;
   run;

The following statements display (in Output 55.4.1) output parameter estimates from PROC MIXED for the first two imputed data sets:

   proc print data=mixparms (obs=8);
      var _Imputation_ Effect Estimate StdErr;
      title 'MIXED Model Coefficients (First Two Imputations)';
   run;

The following statements display (in Output 55.4.2) the output covariance matrices associated with the parameter estimates from PROC MIXED for the first two imputed data sets:

   proc print data=mixcovb (obs=8);
     var _Imputation_ Row Effect Col1 Col2 Col3 Col4;
     title 'Covariance Matrices (First Two Imputations)';
   run;

Note that the variables Col1, Col2, Col3, and Col4 are used to identify the effects Intercept, RunTime, RunPulse, and RunTime*RunPulse, respectively, through the variable Row.

For univariate inference, only parameter estimates and their associated standard errors are needed. The following statements use the MIANALYZE procedure with the input PARMS= data set to produce univariate results:

   proc mianalyze parms=mixparms edf=28;
      modeleffects Intercept RunTime RunPulse RunTime*RunPulse;
   run;

The "Variance Information" table in Output 55.4.3 displays the between-imputation, within-imputation, and total variances for combining complete-data inferences.