I would not rely on the results when this occurs.  The problem is that the data does not support the model so that you will need to change the model specification in some way to get interpretable results.  The suggestion to modify the UNEQUALSLOPES option so that you fit a partial proportional odds model is a good place to start.   I will mention as well that although the estimates seem reasonable, clearly they are not, at least collectively, when they a negative predicted probability. ... View more

Assuming that you want to compare the two levels of audiox, you can use the LSMEANS statement. LSMEANS AUDIOX/DIFF; SAS Help Center: Syntax: LSMEANS Statement ... View more

Unfortunately there is not a good way currently to combine Type3 tests, especially when they follow an F-distribution.  MIANALYZE will not allow you to use a TEST statement with a CLASS variable so that approach won't work directly.  One option would be to dummy code the CLASS variable prior to running MIXED and then use a TEST statement in MIANALYZE on the dummy variables.   ... View more

Check the ordering on the BY statement in Proc SORT.  You should first sort by the analysis variable. proc sort data=lsm; by vfat_q _imputation_; run; ... View more

You will need to sort the LsMeans data set and then run MIANALYZE BY VFAT_Q.  See this example as a template. 30698 - How do I combine the LSMEANS and differences in LSMEANS from Proc GLM using Proc MIANALYZE? (sas.com)   If you are also looking to combine the differences then you can follow this example as well by looking at the second example on the page.   ... View more

Even after correcting the omission of the SOLUTION option, you will still need to make a modification of the ParameterEstimates data set in order to have MIANALYZE read it in correctly.  You should follow the example in the usage note linked below. 48700 - Additional examples of combining PROC GLM results in PROC MIANALYZE (sas.com) ... View more

I am not sure there is any good way to do this.  The normal way would be to use Proc MCMC as you mentioned, but because you are using AUGMENT=LIKELIHOOD, that no longer is an option. ... View more

For binary responses, you will still get a nonzero proportion due to interaction even when you do not fit the interaction.  This is because the model is non-linear; the effect attributable to interaction is on the additive difference scale, but the model is being fit on the log or logistic scale.  This is a known and derivable quality of Causal mediation analyses, but it is not clear how you would actually interpret when you don't fit the interaction explicitly.    ... View more

Try sorting first by LABEL proc sort data=estimate_ds ; by label _imputation_; run; ... View more

The default test is whether or not it is equal to 0.  You would want to modify that to test if it is equal to 1.  This can be done by specifying the THETA0=1 option on the MIANALYZE statement. SAS Help Center: PROC MIANALYZE Statement ... View more

No, the first step is not necessary in your case.  It simply generated a sample data set to use.  You should use the GENMOD step on. ... View more

You would need to add the EXP option to the ESTIMATE statement and then combine those estimates specifically.  Something like the example below would work. data test; seed=2534565; do _imputation_=1 to 25; do i=1 to 250; do ind1=0 to 1; do ind2=0 to 1; logit=-1 + .05*ind1-.67*ind2; p=exp(-logit)/(1+exp(-logit)); if ranuni(seed)>p then y=1; else y=0; output; end; end; end; end; run; ods trace on; proc genmod data=test; model y=ind1 ind2 /dist=bin link=log; by _Imputation_; estimate 'Ind1 relative risk' ind1 1/exp; estimate 'Ind2 relative risk' ind2 1/exp; ods output Estimates=est_ds(where=(index(label,'Exp')=1));*Keeps the EXP values and their standard errors; run; proc sort data=est_ds; by label _imputation_; run; proc mianalyze data=est_ds; by label; modeleffects LBetaEstimate; stderr stderr; run;   ... View more