title: Characterising Uncertainty in the Assessment of Medical Devices and Determining Future Research Needs.
abstract: Decisions about the adoption of medical interventions are informed by evidence on their costs and effects. For a range of reasons, evidence relating to medical devices may be limited. The decision to adopt a device early in its life cycle when the evidence base is least mature may impact on the prospects of acquiring further evidence to reduce uncertainties. Equally, rejecting a device will result in no uptake in practice and hence no chance to learn about performance. Decision options such as 'only in research' or 'approval with research' can overcome these issues by allowing patients early access to promising new technologies while limiting the risks associated with making incorrect decisions until more evidence or learning is established. In this paper, we set out the issues relating to uncertainty and the value of research specific to devices: learning curve effects, incremental device innovation, investment and irrecoverable costs, and dynamic pricing. We show the circumstances under which an only in research or approval with research scheme may be an appropriate policy choice. We also consider how the value of additional research might be shared between the manufacturer and health sector to help inform who might reasonably be expected to conduct the research needed. © 2017 The Authors. Health Economics published by John Wiley & Sons, Ltd.
authors.affiliation: Centre for Health Economics, University of York, York, UK.
authors.forename: Claire
authors.initials: C
authors.lastname: Rothery
authors.affiliation: Centre for Health Economics, University of York, York, UK.|Department of Economics and Related Studies, University of York, York, UK.
authors.forename: Karl
authors.initials: K
authors.lastname: Claxton
authors.affiliation: Centre for Health Economics, University of York, York, UK.
authors.forename: Stephen
authors.initials: S
authors.lastname: Palmer
authors.affiliation: Department of Applied Economics, University of Granada, Granada, Spain.
authors.forename: David
authors.initials: D
authors.lastname: Epstein
authors.affiliation: Centre for Research on Health and Social Care Management, Bocconi University, Milan, Italy.|Department of Policy Analysis and Public Management, Bocconi University, Milan, Italy.
authors.forename: Rosanna
authors.initials: R
authors.lastname: Tarricone
authors.affiliation: Centre for Health Economics, University of York, York, UK.
authors.forename: Mark
authors.initials: M
authors.lastname: Sculpher
mesh_terms: D001499:Bayes Theorem; D035843:Biomedical Research / Q000191:economics / Q000379:methods / Q000592:standards*; D003365:Costs and Cost Analysis; D004864:Equipment and Supplies / Q000191:economics / Q000592:standards*; D019317:Evidence-Based Medicine / Q000191:economics / Q000379:methods / Q000592:standards*; D006801:Humans; D059032:Learning Curve; D020380:Needs Assessment; D013673:Technology Assessment, Biomedical / Q000191:economics / Q000379:methods / Q000592:standards*; D035501:Uncertainty
keywords: MedtecHTA; cost-effectiveness; health technology assessment; medical devices; only in research; uncertainty