OBJECTIVES: Cost-effectiveness analysis (CEA) of biomarkers is challenging due to the indirect impact on health outcomes, and the lack of sufficient fit-for-purpose data. Hands-on guidance is still lacking. Therefore, the aim of this study is twofold, firstly to explore how CEAs in the context of three different types of biomarker applications have dealt with these challenges and secondly, to develop recommendations for future CEAs.

METHODS: A scoping review was performed for three types of biomarker applications: predictive testing, prognostic testing, and serial testing, where we focused on, respectively, advanced non-small cell lung cancer, early-stage colorectal cancer, and all stage colorectal cancer. From the included publications, information was extracted on the reported outcomes, evidence base, data linkages for test and treatment effects, and sensitivity analyses. Recommendations were developed based on the literature findings and expert input.

RESULTS: In total, 43 CEAs were included (31 predictive, 6 prognostic, and 6 serial testing). Three studies based their analyses on end-to-end studies, while the remaining studies used separate sources for test and treatment parameters. While each biomarker application faced the same challenges, differences were observed in the execution of the CEAs amongst the biomarker applications (in inclusion of test performance, assumptions for data linkage of test to treatment evidence, and reporting relevant outcomes). Based on these findings and expert input, seven recommendations were formulated to improve future modeling practices, so future CEAs of biomarkers include all relevant aspects of testing, report relevant outcomes, and explore the impact of important uncertainties.

CONCLUSIONS: Our study has identified various approaches for dealing with challenges in CEAs of biomarker tests for three different biomarker applications. Implementing our recommendations will enhance comprehensive and accurate evaluations specific to the biomarker application of interest, ultimately improving the evaluation process and enhancing the implementation of biomarker applications.