Modelling the Cost-Effectiveness of Diagnostic Tests

Abstract

Diagnostic tests are used to determine whether a disease or condition is present or absent in a patient, who will typically be suspected of having the disease or condition due to symptoms or clinical signs. Economic evaluations of diagnostic tests (e.g. cost-effectiveness analyses) can be used to determine whether a test produces sufficient benefit to justify its cost. Evidence on the benefits conferred by a test is often restricted to its accuracy, which means mathematical models are required to estimate the impact of a test on outcomes that matter to patients and health payers. It is important to realise the case for introducing a new test may not be restricted to its accuracy, but extend to factors such as time to diagnosis and acceptability for patients. These and other considerations may mean the common modelling approach, the decision tree, is inappropriate for underpinning an economic evaluation. There are no consensus guidelines on how economic evaluations of diagnostic tests should be conducted—this article attempts to explore the common challenges encountered in economic evaluations, suggests solutions to those challenges, and identifies some areas where further methodological work may be necessary.