I have a binary logistic regression with just one binary fixed factor predictor. The reason I don't do it as a Chi square or Fisher's exact test is that I also have a number of random factors (there are multiple data points per individual and individuals are in groups, although I don't care about coefficients or significances for those random variables). I do this with R glmer.
I would like to be able to express the coefficient and associated confidence interval for the predictor as a risk ratio rather than an odds ratio. This is because (maybe not for you but for my audience) risk ratio is much easier to understand. Risk ratio here is the relative increase in chance of the outcome being 1 rather than 0 if the predictor is 1 rather than 0.
The odds ratio is trivial to get from the coefficient and associated CI using exp(). To convert an odds ratio to a risk ratio, you can use "RR = OR / (1 – p + (p x OR)), where p is the risk in the control group" (source: http://www.r-bloggers.com/how-to-convert-odds-ratios-to-relative-risks/). But, you need the risk in the control group, which in my case means the chance that the outcome is 1 if the predictor is 0. I believe the intercept coefficient from the model is in fact the odds for this chance, so I can use prob=odds/(odds+1) to get that. I'm pretty much so-far-so-good on this as far as the central estimate for the risk ratio goes. But what worries me is the associated confidence interval, because the intercept coefficient also has its own associated CI. Should I use the central estimate of the intercept, or to be conservative should I use whatever limits of the intercept CI make my relative risk CI widest? Or am I barking up the wrong tree entirely?