Abstract
In this paper we model the cost-benefit of excluding populations at risk through predictive toxicity biomarkers and diagnostics.
False positives/ negatives inherent in predictive markers and the frequency and nature of adverse events determine whether biomarkers are beneficial and economically viable.
We present a model that takes these and other factors into account using data largely in line with real world cases.