As we continue to move into the Proc GLIMMIX world, we are using more non-Gaussian data, such as binomial, Poisson, etc… When we take advantage of the strength of GLIMMIX and designate a non-Gaussian distribution, our LSMeans return to us in a transformed format. But, GLIMMIX has a great option called ILINK to be added to your LSMeasn statement that provides you with the “raw” mean format in your LSMeans table – essentially back-transforming your data so you can present your results in a format that your readers will relate to.
However, if your data conform to a LOGNORMAL distribution, the ILINK option will not work. You are then left with the same challenges we were left with before. What do we do? DO we back-transform the results? Do we present our results in a log format? Will our reader understand that?
I will confess that I have been one of those folks that always struggled with the concept of back-transformations. They don’t always work the way you think they should. And there was always THAT question – Can I back-transform my standard errors? And let;s be brutally honest, most folks used Excel or a calculator to run those back-transformations – right or wrong – didn’t matter.
So, along comes GLIMMIX with all these wonderful options for all of our non-Gaussian distributions except Lognormal. But, hang on, do not despair, there has been a lot of work conducted in this area and we now have wonderful coding options that are easy to implement and provide you with options for back-transforming the standard errors too.
Two methods are available to you now – the Delta method or the Omega method.
This link contains a SAS program with coding for both methods.
SASsy Fridays 
THIS WORKS!!!!!. I HAVE STRUGGLE WITH THIS FOR SOME TIME. THANKS FOR POSTING THIS
Glad it worked 🙂
Very clear! Thank you very much.
Is it possible to apply it also for differences (among groups) obtained from the glimmix procedure?
I might question why you want to do this? If you are running the analysis to determine whether there are differences between the means – you should use the log data and use lsmeans to determine the differences – and report those results. Rather than back transforming the differences and using those values to determine differences – this would not be the correct approach. I hope this helps.