HEALTH ECONOMICS LETTER PRICING OF MEDICAL DEVICES UNDER COVERAGE UNCERTAINTY—A MODELLING APPROACH ALAN J. GIRLING a, *, RICHARD J. LILFORD a and TERRY P. YOUNG b a University of Birmingham, UK b Brunel University, UK SUMMARY Product vendors and manufacturers are increasingly aware that purchasers of health care will fund new clinical treatments only if they are perceived to deliver value-for-money. This influences companies’ internal commercial decisions, including the price they set for their products. Other things being equal, there is a price threshold, which is the maximum price at which the device will be funded and which, if its value were known, would play a central role in price determination. This paper examines the problem of pricing a medical device from the vendor’s point of view in the presence of uncertainty about what the price thresh- old will be. A formal solution is obtained by maximising the expected value of the net revenue function, assuming a Bayesian prior distribution for the price threshold. A least admissible price is identified. The model can also be used as a tool for analys- ing proposed pricing policies when no formal prior specification of uncertainty is available. Copyright © 2011 John Wiley & Sons, Ltd. Received 17 October 2008; Revised 3 August 2011; Accepted 5 October 2011 KEY WORDS: medical devices; reimbursement decisions; commercial pricing; Bayesian prior 1. INTRODUCTION This note addresses the business decision of determining a price for a new medical device with a future funding decision in view. The value of the product to the health care system may result from a balancing of costs and benefits—as in traditional cost-effectiveness analysis—or may be driven by cost considerations. Either way, the funding decision will be sensitive to the price proposed. Nevertheless, funding decisions do not always translate into guaranteed uptake for a technology: the vendor may face a downward-sloping demand curve even after the funding hurdle is negotiated. Funding uncertainty derives from several sources. Some are tied to the performance in use of the new device, others to the perception of the economic value of its benefits; and even when performance and value are established, exogenous political or equitable considerations may perturb a decision away from that sup- ported by a narrow economic analysis. In our model, the impact of uncertainty on the funding decision is cap- tured by a Bayesian prior distribution for the maximum price at which the device would be funded, taking account of the organisational scenario in which the decision will be made. The prior represents the beliefs on which the vendor’s pricing policy will be based. It might be formally elicited as a representation of subjec- tive opinion (O’Hagan et al., 2006) or obtained from a Bayesian analysis of an early stage health economic model (Vallejo-Torres et al., 2008). *Correspondence to: Department of Public Health, Epidemiology and Biostatistics, 90 Vincent Drive, University of Birmingham, Birmingham B15 2TT, UK. E-mail: A.J.Girling@bham.ac.uk Copyright © 2011 John Wiley & Sons, Ltd. HEALTH ECONOMICS Health Econ. (2011) Published online in Wiley Online Library (wileyonlinelibrary.com). DOI: 10.1002/hec.1807