I have a somewhat overlapping question which regards the prediction of pathological response (as per RECIST) - it may also be related to @Pavlos_Msaouel thread here.
Suppose you demonstrate that a biomarker accurately predicts pathological response to cancer treatment in a prospective cohort study.
How do you know if this biomarker is prognostic or predictive?
In other words, how do you tell apart prognosis from (relative) treatment effect?
Any reason to see the prediction of pathological responses “closer” to the prediction of treatment effects?
The resulting paradox is:
(a) if predicting response is mostly predicting (relative) treatment effect, then patients with a high probability of response should undergo the proposed therapy.
(b) if predicting response is mostly predicting prognosis, then patients with a low probability of response should undergo the proposed therapy. (worse prognosis → higher absolute effects)
Perhaps it’s just language, but I can’t help feeling (b) doesn’t make much sense. I think an ideal workflow would include testing an interaction term for relative efficacy in an RCT. In practice, it feels like biomarkers predictive of response found in observational studies are then tested in animal models to get a better idea about relative treatment effects – so the factor separating (a) and (b) would be biological understanding.