Quantifying the Added Value of a Diagnostic Test or Marker

Abstract

Abstract In practice, the diagnostic workup usually starts with a patient with particular symptoms or signs, who is suspected of having a particular target disease. In a sequence of steps, an array of diagnostic information is commonly documented. The diagnostic information conveyed by different results from patient history, physical examination, and subsequent testing is to varying extents overlapping and thus mutually dependent. This implies that the diagnostic potential of a test or biomarker is conditional on the information obtained from previous tests. A key question about the accuracy of a diagnostic test/biomarker is whether that test improves the diagnostic workup beyond already available diagnostic test results. This second report in a series of 4 gives an overview of several methods to quantify the added value of a new diagnostic test or biomarker, including the area under the ROC curve, net reclassification improvement, integrated discrimination improvement, predictiveness curve, and decision curve analysis. Each of these methods is illustrated with the use of empirical data. We reiterate that reporting on the relative increase in discrimination and disease classification is relevant to obtain insight into the incremental value of a diagnostic test or biomarker. We also recommend the use of decision-analytic measures to express the accuracy of an entire diagnostic workup in an informative way.