Towards Recommendations for Cost-Effectiveness Analysis of Predictive, Prognostic, and Serial Biomarker Tests in Oncology

Abstract

Background: 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 lacking.

Objective: We aimed firstly to explore how CEAs in the context of three different types of biomarker applications have addressed these challenges, and secondly to develop recommendations for future CEAs.

Methods: A scoping review was performed for three biomarker applications: predictive, prognostic, and serial testing, in advanced non-small cell lung cancer, early-stage colorectal cancer, and all-stage colorectal cancer, respectively. Information was extracted on the model assumptions and uncertainty, and the reported outcomes. An in-depth analysis of the literature was performed describing the impact of model assumptions in the included studies.

Results: A total of 43 CEAs were included (31 predictive, 6 prognostic, and 6 serial testing). Of these, 40 utilized different sources for test and treatment parameters, and three studies utilized a single source. Test performance was included in 78% of these studies utilizing different sources, but this parameter was differently expressed across biomarker applications. Sensitivity analyses for test performance was only performed in half of these studies. For the linkage of test results to treatments outcomes, a minority of the studies explored the impact of suboptimal adherence to test results, and/or explored potential differences in treatment effects for different biomarker subgroups. Intermediate outcomes were reported by 67% of studies.

Conclusions: We identified various approaches for dealing with challenges in CEAs of biomarker tests for three different biomarker applications. Recommendations on assumptions, handling uncertainty, and reported outcomes were drafted to enhance modeling practices for future biomarker cost-effectiveness evaluations.

Fig. 1

A simplified schematic overview of the biomarker applications and their potential impact on subsequent clinical decisions. This figure illustrates how biomarker tests inform clinical decision making across various applications. Predictive testing involves identifying a biomarker where the test result indicates the presence (positive result) or absence (negative result) of the target biomarker. The test results can be further classified as true or false, depending on the clinical sensitivity and specificity of the test. Prognostic testing predicts the risk of disease recurrence within a patient population, often due to biological differences between subgroups. A prognostic biomarker stratifies patients into high-risk or low-risk groups. The greater the difference in recurrence risk between these subgroups, the more effective the biomarker is in accurately categorizing patients. Serial testing involves repeated testing to detect disease recurrence or progression. Like predictive testing, both positive and negative results in serial testing can be classified as true or false, based on the sensitivity and specificity. CRC colorectal cancer, NSCLC non-small cell lung cancer

Fig. 2

Reported intermediate outcomes for predictive testing cost-effectiveness analyses. *Accuracy-related outcomes include true positives and negatives, false positives and negatives, suboptimal received treatments (treatments based on false negatives and false positives) and correct treatment decisions. RCT randomized controlled trial