Classical logistic regression VS Firth logistic regression: comparison in power I understand that in case of separated data, logistic regression via ordinary MLE has an upward bias in the p values, which implies that any penalized MLE designed to reduce this bias will have more power in such cases. Specifically I'm considering Firth's penalization.
Can someone point me to a reference or an argument that compares the power of Firth logistic regression to ordinary MLE in general (that is, without the assumption of data separation)?

With some more research I found this article where several methods are compared for power amongst other features, and they find that Firth regression has a power comparable to ordinary GLM:
https://www.nature.com/articles/s41598-021-82547-z.pdf
