Clinical biomarker innovation: when is it worthwhile?

Abstract

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.