A New Model Selection Metric for Biomarker Detection Algorithms and Tools

Abstract

In the era of precision medicine, biomarker plays a vital role in drug clinical trials. It helps select the patients more likely to respond to the therapy and increases the possibility of success of the trial. Model selection is critical in the development of the algorithm. Traditional model selection metrics ignore two clinical utilities of the biomarker in drug clinical trials, one is its ability to distinguish positive and negative patients in terms of treatment effect and another is the total cost of the biomarker-based drug clinical trial. We proposed a new model selection metric that estimates the above two clinical utilities of biomarker detection algorithms without the need for a real drug clinical trial. In the simulation, we will compare the proposed metric with the widely used ROC-based metric in selecting the optimal cutoff value for the model and discuss which one to choose under various circumstances.