Diagnostic Testing and Decision-Making: Beauty Is Not Just in the Eye of the Beholder

Abstract

To use a diagnostic test effectively and consistently in their practice, clinicians need to know how well the test distinguishes between those patients who have the suspected acute or chronic disease and those patients who do not. Clinicians are equally interested and usually more concerned whether, based on the results of a screening test, a given patient actually: (1) does or does not have the suspected disease; or (2) will or will not subsequently experience the adverse event or outcome. Medical tests that are performed to screen for a risk factor, diagnose a disease, or to estimate a patient’s prognosis are frequently a key component of a clinical research study. Like therapeutic interventions, medical tests require proper analysis and demonstrated efficacy before being incorporated into routine clinical practice. This basic statistical tutorial, thus, discusses the fundamental concepts and techniques related to diagnostic testing and medical decision-making, including sensitivity and specificity, positive predictive value and negative predictive value, positive and negative likelihood ratio, receiver operating characteristic curve, diagnostic accuracy, choosing a best cut-point for a continuous variable biomarker, comparing methods on diagnostic accuracy, and design of a diagnostic accuracy study.