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Abstract
The successful use of a targeted therapy is intrinsically linked to the ability of a companion diagnostic to correctly identify patients most likely to benefit from treatment. The aim of this study was to review the characteristics of companion diagnostics that are of importance for inclusion in an economic evaluation. Approaches for including these characteristics in model-based economic evaluations are compared with the intent to describe best practice methods. Five databases and government agency websites were searched to identify model-based economic evaluations comparing a companion diagnostic and subsequent treatment strategy to another alternative treatment strategy with model parameters for the sensitivity and specificity of the companion diagnostic (primary synthesis). Economic evaluations that limited model parameters for the companion diagnostic to only its cost were also identified (secondary synthesis). Quality was assessed using the Quality of Health Economic Studies instrument. 30 studies were included in the review (primary synthesis n = 12; secondary synthesis n = 18). Incremental cost-effectiveness ratios may be lower when the only parameter for the companion diagnostic included in a model is the cost of testing. Incorporating the test’s accuracy in addition to its cost may be a more appropriate methodological approach. Altering the prevalence of the genetic biomarker, specific population tested, type of test, test accuracy and timing/sequence of multiple tests can all impact overall model results. The impact of altering a test’s threshold for positivity is unknown as it was not addressed in any of the included studies. Additional quality criteria as outlined in our methodological checklist should be considered due to the shortcomings of standard quality assessment tools in differentiating studies that incorporate important test-related characteristics and those that do not. There is a need to refine methods for incorporating the characteristics of companion diagnostics into model-based economic evaluations to ensure consistent and transparent reimbursement decisions are made.
Acknowledgements
B Doble is supported by research scholarships from Monash University. P Lorgelly is a recipient of a Victorian Government Translational Research Grant through the Victorian Cancer Agency. The funding sources had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review and approval of the manuscript or decision to submit the manuscript for publication.
Financial & competing interests disclosure
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties.
No writing assistance was utilized in the production of this manuscript.
The successful use of a targeted therapy is intrinsically linked to the ability of a companion diagnostic to correctly identify patients most likely to benefit from treatment.
This ability can be directly related to various characteristics of the companion diagnostic and therefore the economic value of these tests and targeted therapies should be assessed in combination.
Varying methodological approaches (e.g., consideration of the impact of the test on improper diagnoses or only considering the costs associated with testing) for incorporating a companion diagnostic into a model-based economic evaluation have been published in the literature.
Incremental cost-effectiveness ratios may be smaller when only the cost of testing is considered in a model compared with additionally incorporating the impact of the test’s sensitivity and specificity on treatment decisions and therefore the former may be an inappropriate methodological approach.
Altering certain companion diagnostic test characteristics (e.g., prevalence of the genetic biomarker, specific population tested, type of test, test accuracy and timing/sequence of multiple tests) can have an impact on overall model results.
Similar overall quality scores were obtained for studies included in both the primary and secondary syntheses using standard metrics, despite studies from the secondary synthesis using inappropriate methods for including a companion diagnostic into a model (i.e., consideration of only testing costs). This calls into question the usefulness of standard quality assessment tools for economic evaluations of companion diagnostics and targeted therapies and the need to consider additional quality criteria.
Future studies assessing the economic value of companion diagnostics and their associated targeted therapies should consider our methodological checklist and look toward high-quality examples existing in the literature to guide their methodological approach.
Further research is required to refine methods for incorporating the characteristics of companion diagnostics into model-based economic evaluations and fully understand how different approaches can affect the cost-effectiveness of a test and treat strategy to ensure consistent and transparent reimbursement decisions are made.
The potential use of multiplex and next-generation sequencing testing further complicates the application of model-based economic evaluations for determining the value of a test and treat strategy in oncology. Further research from a multidisciplinary perspective will be required to ensure the appropriate economic assessment of these more complex diagnostic testing methods and associated targeted therapies.