From: Cost-effectiveness analyses of genetic and genomic diagnostic tests (Must read)
| Type of challenge | Example of challenge | Description of challenge |
|---|---|---|
| Methodological | Selecting the appropriate evaluative framework | Is the standard extra-welfarist view and use of CEA appropriate, or should the distinct theoretical approach reflecting the welfarist view and use of CBA be adopted to allow consequences other than health gain, such as the value of diagnostic information from the genomic-targeted diagnostic test, to be valued? |
| Relevant study perspective | Is the standard recommendation to focus on the use of health-care services appropriate when the genomic-targeted diagnostic test may provide information that affects the use of other services, such as education or employment? | |
| Relevant time horizon | Is a lifetime sufficient when the impact of a genomic-targeted diagnostic test may extend to infinite time horizons that are not limited by the lifespan of one individual? | |
| Defining the relevant study population | Is the standard definition of a patient (the person receiving the technology) appropriate when there could be spillover effects to family members (currently alive or to be born) as a result of information from a genomic-targeted diagnostic test? | |
| Valuing consequences | Is identifying and measuring the impact on health status alone sufficient to capture the (good and bad) consequences of a genomic-targeted diagnostic test? | |
| Technical | Variation in the individual characteristics of the relevant study population | The use of cohort state transition Markov models, sometimes combined with decision trees, cannot easily capture the impact of individual patient variation within a population with different genotypes and phenotypes |
| Number of diagnostic and, if appropriate, subsequent treatment pathways | The use of cohort state transition Markov models, sometimes combined with decision trees, cannot easily account for multiple comparators often needed when evaluating a new genomic-targeted diagnostic test | |
| Capturing impact of reduced time to diagnosis | The use of cohort state transition Markov models, sometimes combined with decision trees, cannot account for the impact of reduced time to achieve a diagnosis, which is often a proposed benefit of a genomic-targeted diagnostic test | |
| Capturing impact of capacity constraints | Decision analytic model-based CEA currently assumes limitless capacity within health-care systems, which is often not a reasonable assumption when introducing a genomic-targeted diagnostic test to populations for whom a diagnosis was not previously available | |
| Practical | Availability of data | There is often a lack of data available to populate decision analytic model-based CEA |
| National tariff of test cost | No national tariff for genomic-targeted tests exist | |
| Organizational | Complex health-care systems | Decision analytic model-based CEA assumes that money saved and benefits accrued are transferable, but this is often challenging in complex health-care systems that comprise an overarching funding mechanism (public, private, insurance), a service and staffing model for providing care for different sectors (community, general practice, hospital, specialist) and a means of allocating funding to these different sectors |
| Generalizability of results | Decision analytic model-based CEA is relevant only to the defined decision problem, and decision-makers who want to use the results must decide whether the focus of the analysis is relevant to their own jurisdiction | |
| Expensive nature of health technology assessment | Decision analytic model-based CEA conducted within national health technology assessment processes requires considerable funding and expertise that are not available to all, which may contribute to the inequity in access to new genomic-targeted diagnostic tests across the world |
- CBA, cost-benefits analysis; CEA, cost-effectiveness analysis.
