From differences in means between cases and controls to risk stratification: a business plan for biomarker development.

UNLABELLED: Researchers developing biomarkers for early detection can determine the potential for clinical benefit at early stages of development. We provide the theoretical background showing the quantitative connection between biomarker levels in cases and controls and clinically meaningful risk measures, as well as a spreadsheet for researchers to use in their own research. We provide researchers with tools to decide whether a test is useful, whether it needs technical improvement, whether it may work only in specific populations, or whether any further development is futile. The methods described here apply to any method that aims to estimate risk of disease based on biomarkers, clinical tests, genetics, environment, or behavior. SIGNIFICANCE: Many efforts go into futile biomarker development and premature clinical testing. In many instances, predictions for translational success or failure can be made early, simply based on critical analysis of case–control data. Our article presents well-established theory in a form that can be appreciated by biomarker researchers. Furthermore, we provide an interactive spreadsheet that links biomarker performance with specific disease characteristics to evaluate the promise of biomarker candidates at an early stage.