# Incidence Rate Ratio (IRR) in R from linear regression using log-transformed data?

I was wondering if it would make sense to calculate IRR for OLS (not poisson), but the OLS is done using log-transformed data? I have a set of crude death rate data (which I'm still debating whether they are count data (because they are, after all, based on counts) or continuous data (since they are not integers), and I've modeled them using poisson, but then just curious what would happen if I logged the crude rate and then perform a robust linear regression.... but I'd like to compare the two ways via IRR.....

any suggestions welcome, for example, if I really shouldn't log the crude rate to begin with..... thanks!

Well, if your numerator is directly interpreted as counts, then both the poisson regression and the log transformed outcome linear regression will be consistent for the same parameters. The only discrepancy in this case is exactly how the observations are weighted (see paragraph 2). If your outcome is rates and you have measured (variable) denominators (such as 1-3 $\mu$gs of biopsied tumor, or 1-20 ccs blood), you need to use some alternative approaches to account for the various weighting differences in the two groups. In both linear regression and Poisson regression, this comes about in the form of an offset. I'm curious whether this should be a consideration in your problem.
Although R warns you about non-integral counts in Poisson GLMs, there are plenty of sane regression models, especially in, say, ecology, where non-integral Poisson outcomes come about such as plankton concentration in a cubic meter of sampled water from various watersheds, or flow cytometry assessed mRNA concentration in biopsied tumor tissue.