Back Transformations for Lognormal Data

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.

 

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