# Seeking to understand using the Firth correction in Generalized Estimating Equations to deal with quasi-complete separation

In order to deal with complete separation in my data someone suggested that I run penalized GEE (PGEE) by adding a Firth-type penalty term in R.

Although I have read many papers on the Firth correction, I am still not feeling comfortable that I completely understand the concept of Firth correction, as those papers require a much deeper statistical background than I have.

Is there any way to explain this in plain language?

If your sample size $$n$$ is small or moderate, it turns out that the MLE estimators of these effects suffer from bias under the ideal scenario, with the magnitude of the bias depending on $$n$$.
As explained in https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-9-56, this bias can be expressed as the additive sum of bias components which are proportional to $$1/n$$, $$1/n^2$$, etc. The bias component which is proportional to the inverse of the sample size (i.e., $$1/n$$) is called 'first order' bias component. (As the sample size increases, the bias converges to 0.)