piece of information for a reimbursement decision is knowing whether the bio-
marker is a treatment effect modifier or a prognostic factor, i.e., whether targeting
the biomarker varies the likely clinical benefit of the drug, and if so, to what
extent,^28 which is also in some cases difficult to monitor and model. Moreover,
diagnostic tests rarely affect patient’s health status directly. On the contrary, the test
mainly affects the decision whether to initiate a treatment or not and in what dosage.
Therefore, most direct benefits of (companion) diagnostics to patients arise from the
treatments that follow the test, not the diagnostic test itself, and hence the value of
the test is best understood in the context of its effect on the treatment plan, care, and
monitoring. Given the great amount of information that defines the total care
pathway, from treatment to monitoring, the evaluation of the effect of a companion
diagnostic on patient’s health is very complex. Moreover, single diagnostic tests are
frequently ordered alongside other tests, and their individual contribution to care
pathway is hard to distinguish, which additionally complicates the assessment of
their (cost-) effectiveness. Since clinical studies of diagnostic tests rarely follow
patients through treatments to their final outcomes, large pieces of data and
information on the appropriate outcomes are regularly not available.^29
In spite of the lack of data, the evaluation of diagnostics still requires the process
of their use and their benefits to be described, measured, and valued—if companion
diagnostic products will be used wisely. However, if relevant information for
populating decision trees and models is missing, economic analyses may yield
results of questionable validity and the uncertainty surrounding the result may be
very large. This, in turn, increases the uncertainty with which decisions regarding
the use of diagnostic technologies can be made and decreases the usefulness of the
results of economic evaluations in guiding reimbursement decisions. These impor-
tant differences make the evaluation of diagnostics more complex than the assess-
ments of traditional pharmaceuticals, and they may hinder the usefulness of
economic evaluations in the policy making realm.
3.2 Assessments of Companion Diagnostics: Some
Applications
Companion diagnostic tests (whether molecular, genetic, or based on some other
technology) need to be subjected to assessment to determine their impact on health
outcomes and health care budgets and ultimately to calculate real-world cost-
effectiveness. Given that new health technologies are the main (although not the
sole) driver of rising costs in health care in all jurisdictions, as Fisher et al. ( 2009 )
(^28) Discussed further by Merlin et al. ( 2013 ), pp. 333–342.
(^29) The issue is further discussed in NICE DAP document available athttp://www.nice.org.uk/
Media/Default/About/what-we-do/NICE-guidance/NICE-diagnostics-guidance/Diagnostics-assess
ment-programme-manual.pdf.
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