Hi all, i’m new here but have taken a lot from these forums over the last months. This is I think a very simple question.
There’s debate in epi regarding the relative utility of odds vs risks in studies with binary outcomes. The former have favourable mathematical properties (not constrained to be between 0 and 1), while the latter are more intuitive and easier to interpret.
Some have suggested rather than estimate odds ratios, one can use log binomial models (which often fail to converge) or poisson models with a robust variance estimator to estimate risk ratios directly. This makes sense to me.
But why can’t we just fit logistic regression models to calculate the fitted odds and then use the inverse logit transformation to calculate risks (probabilities)?? For a continuous exposure, we could then plot the probability of the outcome across the entire range of the exposure. For categorical exposures, this would give us the (adjusted) risks in each group.
But I’ve never seen this done so I presume that it’s incorrect. My intuition about why this is wrong is related to the sigmoidal shape of the logistic function, but I still can’t quite manage to grasp it… If anyone could help me clarify this, it would be much appreciated.