Not quite. I visualize three or four rounds of imputation for all missing independent variables, simultaneously. Then, for each dependent variable/independent variable combination, you run PROC GLM with a by imputation statement. You can then combine these analyses using MIANALYZE, one for each dependent/independent variable combination.
Now, if (and only if) you have the same independent variables for ALL of the dependent variables, you would run one GLM in multivariate mode, with a by imputation statement, followed by one MIANALYZE to combine these analyses.
For instance, suppose you had y1, y2 and y3 as dependent variables, and x1, x2, x3, and x4 as independent variables. You would need to run PROC MI to impute any missing values in x1, x2, x3 and x4--say three to five imputations. Then suppose you wanted to fit y1 from x1 and x2, y2 from x3 and x4, and y3 from x1, x3 and x4. Your GLM statements would look something like:
proc glm data=imputeddata;
by _imputation_:
class x1 x2;
model y1=x1 x2/inverse;
ods output ParameterEstimates=glmparms_y1 InvXPX=glmxpxi_y1;
quit;
proc mianalyze parms=glmparms_y1 xpxi=glmxpxi_y1 edf=(you insert the correct degrees of freedom based on number of imputed values in x1 and x2);
modeleffects intercept x1 x2;
run;
You would then repeat this for y2, using x3 and x4, and for y3 using x1, x3, and x4.
Now suppose you use x1, x2, x3, and x4 for y1, y2, and y3. You might try:
proc glm data=imputeddata;
by _imputation_:
class x1 x2 x3 x4;
model y1 y2 y3=x1 x2 x3 x4/inverse;
ods output ParameterEstimates=glmparms_y1 InvXPX=glmxpxi_y1;
quit;
proc mianalyze parms=glmparms_y1 xpxi=glmxpxi_y1 edf=(you insert the correct degrees of freedom based on number of imputed values in all of the xi's);
modeleffects intercept x1 x2 x3 x4;
run;
This, I assume would give univariate analyses for y1, y2 and y3.
Accommodating any multivariate relationship among y1, y2 and y3, using the MANOVA statement in GLM may be possible, but I would say "Get in touch with Tech Support" for something like that.
Steve Denham
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