I have a data set with a dichotomous outcome variable (surgery result good/bad) and two MR scan markers that are continuous (measurements on the scan).
Now if you have a large measurement in one marker, you probably also have a large measurement in the other marker.
When regressing the relationship with one variable at a time, there is no significance, but if I regress both together, there is a high significance, that is it predicts a good surgery outcome if you have a larger marker in either one.
Does this mean that the measurement cannot predict surgery results on its own, but if both markers are enlarged, it can predict surgery results?
Note that the model does run without multicollinearity problems.