I'm analysing some data for a study that has a normal (cognition) outcome, but the predictor of interest (biomarker) is heavily skewed at the top end. The vast majority of the data is pooled around very low numbers.
Log transformations probably wouldn't normalise the data and as I understand they make interpretation quite difficult, so that's not something I want to look at really.
My Question is - does anyone have any advice on model structure given this? Should the non-normal covariate be broken down into quartiles, as is common in the lit? Or would a traditional linear regression produce reasonable estimates? Or perhaps something like a generalized estimating equation would be more appropriate?