Hello, I was wondering if someone had experience working with count data and attempted to use PROC CAUSALMED I am working with count data and I am attempting an mediation analysis with x=continuous m=binary and y=binary. From the Baron and Kenny method, we were able to see complete mediation, but am attempting to gain statistical inference with PROC CAUSALMED and entered the following syntax: PROC CAUSALMED data=x all; class _med_bin/ref=last; model _outc_bin= _exp_cont _med_bin/dist=poisson link=log; mediator _med_bin = _outc_bin; run; However, I only was able to get the effects (TE, CDS, NDE, and NIE) already exponentiated (as if they were odd ratios) as opposed to being raw estimates with the confidence intervals already having 1.00 within it (Ex. total effect being 1.01 with 95% CI of 0.95 - 1.08) but showing a significant z-score (p < 0.001) Even if I walked backwards and manually calculate from the transformed effects, I am confused why 1) how to obtain the standard error untransformed and 2) the confidence intervals cross the null (the lower CI's below 1.00) while the p-values were calculated to be significant. How do I reverse the transformation of the effects within SAS, since it appears that I did not manually calculate from the output correctly? Would there be another way to assess for mediation outside the procedure? I attempted to use the %mediation macro and was unable to get an output that way as well. Thank you
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