MIANALYZE can only combine individual point estimates and cannot be used to combine F-tests specifically.  The theory itself is not very well-developed for combining multiple DF tests, especially Type 3 tests from MIXED, since Rubin’s Rules apply directly to parameter estimates and their variances, not to F-statistics.   MIANALYZE does have a TEST statement and through the MULT option you can get a joint test based on the combined parameter estimates.  It does not allow these options with a CLASS statement, but you might be able to "trick" it into computing a type 3 hypothesis by dummy coding yourself and feeding those estimates into MIANALYZE.  Whether that is statistically valid or not however is not clear however. ... View more

Regarding your first question, there is no optimal data set.  The purpose of using Proc MI is to develop several data sets so as to avoid the problems of bias that is typically associated with single imputation.  To use only one of those data sets would still leave you with most of those issues.  It might be helpful to review the Overview section of the MI documentation to familiarize yourself with its purpose. SAS Help Center: Overview: MI Procedure Multiple imputation inference involves three distinct phases: The missing data are filled in m times to generate m complete data sets. The m complete data sets are analyzed by using standard procedures. The results from the m complete data sets are combined for the inference. MIANALYZE would be used in the 3rd step.  Proc MI is used as the 1st step.  The 2nd step would be to run whatever analytical procedure you are planning to run (such as for a regression you would use Proc REG).   Regarding your second question, if the data is gathered as discrete measurements, then it may be that you need to use the CLASS statement as already suggested.  If instead you want the round the measurements to a certain precision, then you could use the ROUND= option, but you will need to be explicit in mapping it to the variables. So for example, if you want to round variables A and C to the nearest tenth and not round B and D, you would use the following: proc mi data=yourdata out=outdata round=.1 . .1 .; var a b c d; run; Note how the ordering of the values on the ROUND= option matches the ordering of the variables on the VAR statement.   ... View more

This appears to be a defect of some sort.  I would suggest that you contact Technical Support directly so they can investigate the issue. ... View more

The reason for the differences has to do with the fact that all of the variables are standardized using all of the observations prior to fitting the imputation model.  Adding observations changes the mean and variance and thus the standardized values.  These values are then used in the imputation model, which is built only on one of the groups, which leads to slightly different estimates.   To see this more explicitly, look at the example below.  Notice how the "obs-data" estimates change slightly because all the observations are standardized.  This can be readily verified using Proc STANDARD and Proc REG. data Mono1; do Trt=0 to 1; do j=1 to 5; y0=10 + rannor(999); y1= y0 + Trt + rannor(999); if (ranuni(999) < 0.3) then y1=.; output; end; end; do Trt=0 to 1; do j=1 to 45; y0=10 + rannor(999); y1= y0 + Trt + rannor(999); if (ranuni(999) < 0.3) then y1=.; output; end; end; drop j; run; proc mi data=Mono1 seed=14823 nimpute=1 out=outex15; class Trt; monotone reg (/details); mnar model( y1 / modelobs= (Trt='0')); var y0 y1; ods select MonoReg; run; proc standard data=mono1 mean=0 std=1 out=out1; var y0 y1; run; proc reg data=out1; where trt=0; model y1=y0; ods select ParameterEstimates; run; data add; trt=1; do j=1 to 45; y0=10 + rannor(1); y1= y0 + Trt + rannor(999); if (ranuni(999) < 0.3) then y1=.; output; end; drop j; run; data mono2; set mono1 add; run; proc mi data=Mono2 seed=14823 nimpute=15 out=outex15; class Trt; monotone reg (/details); mnar model( y1 / modelobs= (Trt='0')); var y0 y1; ods select MonoReg; run; proc standard data=mono2 mean=0 std=1 out=out2; var y0 y1; run; proc reg data=out2; where trt=0; model y1=y0; ods select ParameterEstimates; run; ... View more

Below is an example that shows the necessary syntax for combining the Proc TTEST results. /*Create a sample data set to work with*/ data test; do grp=1 to 2; do rep=1 to 25; if rand('UNIFORM')>.7 then y=.; else y=rand('NORMAL',.66*grp,1); output; end; end; run; proc mi data=test out=mi_test seed=1; class grp; var grp y; monotone regression; run; /*This code would work for the difference in two means*/ proc ttest data=mi_test; class grp; by _imputation_; var y; ods output statistics=ttest_ds; run; proc sort data=ttest_ds; by class _imputation_; run; proc mianalyze data=ttest_ds; by class; modeleffects mean; stderr stderr; run; ... View more