Eliciting informative priors for coefficients in logistic regression while accounting for baseline risk

[maxbiostat]

Thanks for taking the time @f2harrell, I appreciate it. But I’ll have to disagree. I know you don’t much like RRs, as per this post, for instance, but I don’t think ORs are easy to interpret or any easier to interpret than RRs. The usual difficulty with RRs, knowing the baseline risk, is taken as given for this problem.

@f2harrell

Dealing with nonlinearity would be hard though. You might put priors on the nonlinear terms that favor zero pretty heavily, and a prior on the linear coefficient such that the inter-quartile-range OR has a low chance of being larger than 5 or less than 1/5.

This is a good point. But in the simple case of a logistic regression, is it not the case that the non-linearity of the model is taken into account by the transformations I discuss in my original post?