Abstract

The traditional statistical approach to the evaluation of diagnostic tests, prediction models and molecular markers is to assess their accuracy, using metrics such as sensitivity, specificity and the receiver-operating-characteristic curve. However, there is no obvious association between accuracy and clinical value: it is unclear, for example, just how accurate a test needs to be in order for it to be considered "accurate enough" to warrant its use in patient care. Decision analysis aims to assess the clinical value of a test by assigning weights to each possible consequence. These methods have been historically considered unattractive to the practicing biostatistician because additional data from the literature, or subjective assessments from individual patients or clinicians, are needed in order to assign weights appropriately. Decision analytic methods are available that can reduce these additional requirements. These methods can provide insight into the consequences of using a test, model or marker in clinical practice.