Clinical biomarker innovation: when is it worthwhile?

Background: Choosing which biomarker tests to select for further research and development is not only a matter of diagnostic accuracy, but also of the clinical and monetary benefits downstream. Early health economic modeling provides tools to assess the potential effects of biomarker innovation and support decision-making. Methods: We applied early health economic modeling to the case of diagnosing primary aldosteronism in patients with resistant hypertension. We simulated a cohort of patients using a Markov cohort state-transition model. Using the headroom method, we compared the currently used aldosterone-to-renin ratio to a hypothetical new test with perfect diagnostic properties to determine the headroom based on quality-adjusted life-years (QALYs) and costs, followed by threshold analyses to determine the minimal diagnostic accuracy for a cost-effective product. Results: Our model indicated that a perfect diagnostic test would yield 0.027 QALYs and increase costs by €43 per patient. At a cost-effectiveness threshold of €20,000 per QALY, the maximum price for this perfect test to be cost-effective is €498 (95% confidence interval [CI]: €275–€808). The value of the perfect test was most strongly influenced by the sensitivity of the current biomarker test. Threshold analysis showed the novel test needs a sensitivity of at least 0.9 and a specificity of at least 0.7 to be cost-effective. Conclusions: Our model-based approach evaluated the added value of a clinical biomarker innovation, prior to extensive investment in development, clinical studies and implementation. We conclude that early health economic modeling can be a valuable tool when prioritizing biomarker innovations in the laboratory.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Clinical biomarker innovation: when is it worthwhile? ; volume:57 ; number:11 ; year:2019 ; pages:1712-1720 ; extent:9 ; Clinical chemistry and laboratory medicine ; 57, Heft 11 (2019), 1712-1720 (gesamt 9)

Contributors, Places and Time

• Creator: Kluytmans, Anouck ; Deinum, Jaap ; Jenniskens, Kevin ; van Herwaarden, Antonius Eduard ; Gloerich, Jolein ; van Gool, Alain J. ; van der Wilt, Gert Jan ; Grutters, Janneke P.C.

Further information

• DOI: 10.1515/cclm-2019-0098
• URN: urn:nbn:de:101:1-2022052110291506031633
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:35 AM CEST