Objectives: There is little specific guidance on performing an early cost-effectiveness analysis (CEA) of medical tests. We developed a framework with general steps and applied it to two cases.

Methods: Step 1 is to narrow down the scope of analysis by defining the test's application, target population, outcome measures, and investigating current test strategies and test strategies if the new test were available. Step 2 is to collect evidence on the current test strategy. Step 3 is to develop a conceptual model of the current and new test strategies. Step 4 is to conduct the early-CEA by evaluating the potential (cost-)effectiveness of the new test in clinical practice. Step 5 involves a decision about the further development of the test.

Results: The first case illustrated the impact of varying the test performance on the headroom (maximum possible price) of an add-on test for patients with an intermediate-risk of having rheumatoid arthritis. Analyses showed that the headroom is particularly dependent on test performance. The second case estimated the minimum performance of a confirmatory imaging test to predict individual stroke risk. Different combinations of sensitivity and specificity were found to be cost-effective; if these combinations are attainable, the medical test developer can feel more confident about the value of further development of the test.

Conclusions: A well-designed early-CEA methodology can improve the ability to develop (cost-)effective medical tests in an efficient manner. Early-CEAs should continuously integrate insights and evidence that arise through feedback, which may convince developers to return to earlier steps.

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doi.org/10.1017/S0266462316000064, hdl.handle.net/1765/93158
International Journal of Technology Assessment in Health Care
Erasmus School of Health Policy & Management (ESHPM)

Buisman, L., Rutten-van Mölken, M., Postmus, D., Luime, J., Uyl-de Groot, C., & Redekop, K. (2016). The early bird catches the worm: early cost-effectiveness analysis of new medical tests. International Journal of Technology Assessment in Health Care, 32(1-2), 46–53. doi:10.1017/S0266462316000064