I'm trying to find a way to visualize the results of an association analysis where I corrected for confounding variables.
I have a set of cytokine data (amount of protein in the blood) from a set of patients infected and uninfected with HCV. The difference between the two is minimal when we test/visualize the raw data. However, when I adjusted for things like Race, gender, age, and disease status (by building a simple linear model) the p-values improved drastically.
However, I'm having trouble finding a way to show this visually. Normally I would just show a box-plot or bee-swam plot or something similar. But the raw data is unimpressive.
Does anyone have any ideas on ways to show this beyond reporting a p-value and the confounders/effect sizes?