Comparing diagnostic tests and biomarkers based on benefit-risk under tree orderings of disease classes

The assessment and comparison of biomarkers and diagnostic tests using a benefit-risk framework are essential for evaluating both the accuracy of tests and the clinical implications of diagnostic errors. Traditional measures, such as sensitivity and specificity, often do not fully capture the complexities involved in evaluating tests for diseases with multiple subtypes. Many diseases, such as Alzheimer’s, are characterized by multiple stages or classes, and in some cases, like cancers, these classes do not follow a specific order, necessitating a more nuanced approach. This paper extends the net benefit approach, traditionally applied to binary diagnostic tests, to address clinical conditions with multiple unordered subtypes using a tree or umbrella ordering framework. We introduce a novel methodology that expands the diagnostic yield table to account for multisubtypes, allowing for a more comprehensive evaluation of diagnostic tests. This approach incorporates decision-making processes based on net benefit, offering additional insights into the criteria for ruling in or ruling out clinical conditions and highlighting the potential adverse consequences of unnecessary diagnostic workups. Through numerical examples, simulations, and real-world data applications, we demonstrate the flexibility and potential advantages of our proposed framework in handling complex disease scenarios. By accommodating multiple subtypes and providing a structured approach to evaluating the net benefit of diagnostic tests, this methodology offers valuable insights for clinical decision-making. The framework’s ability to incorporate the specific characteristics of disease subtypes makes it particularly useful in settings where traditional binary classification measures may fall short. This approach could significantly enhance the accuracy of diagnostic evaluations and support more tailored interventions in clinical practice, thereby improving patient outcomes.