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Published online by Cambridge University Press: 31 December 2019
The Institute of Health Economics offers a suite of analyses that provide developers an understanding of the expected commercial viability of an early stage health technology. In combination, these analyses form the Value-Engineered Translation framework. These methods incorporate innovative methods to manage uncertainty in early economic evaluations, in particular, moving beyond current stochastic assessments of headroom to account for inter-market variability in value hurdles, as well as incorporating social value premia considerations. An illustration of these methods is demonstrated using the example of a non-invasive diagnostic test (called DCRSHP) at an early stage of development, compared to current practice of cystoscopy in the diagnosis of bladder cancer.
Competing technologies were identified to inform the headroom assessment based on price and effectiveness. Then, a model-based cost-effectiveness analysis was undertaken incorporating headroom analysis, stochastic one-way sensitivity analysis, and value of information analysis using data from secondary sources.
Currently there are a number of non-invasive tests available, but none have sufficient test accuracy to be suitable for bladder cancer diagnosis alone. From the headroom analysis, DCRSHP can be priced at up to CAD 790 (i.e. USD 588) and still be cost-effective compared to the current practice of cystoscopy. Interestingly this price can be increased for patient groups that have lower levels of bladder cancer prevalence.
The requirements of economic evaluations depend on the stage of technology development, and analysis approaches must reflect this. The results here indicate that DCRSHP clears the value hurdle in terms of being cost-effective, and thus provides the opportunity to make a commercial return on future investment. Future analysis of DCRSHP could consider the cost drivers for development of the technology, including the regulatory pathways, costs associated with the intellectual asset management for the technology, and alternative manufacturing costs. All of which contribute to the research-to-practice continuum.