title: Late-stage pharmaceutical R&D and pricing policies under two-stage regulation.
abstract: We present a model combining the two regulatory stages relevant to the approval of a new health technology: the authorisation of its commercialisation and the insurer's decision about whether to reimburse its cost. We show that the degree of uncertainty concerning the true value of the insurer's maximum willingness to pay for a unit increase in effectiveness has a non-monotonic impact on the optimal price of the innovation, the firm's expected profit and the optimal sample size of the clinical trial. A key result is that there exists a range of values of the uncertainty parameter over which a reduction in uncertainty benefits the firm, the insurer and patients. We consider how different policy parameters may be used as incentive mechanisms, and the incentives to invest in R&D for marginal projects such as those targeting rare diseases. The model is calibrated using data on a new treatment for cystic fibrosis.
authors: Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-412 96, Gothenburg, Sweden.
authors: Sebastian
authors: S
authors: Jobjörnsson
authors: Department of Economics and Related Studies, University of York, Heslington, York YO10 5DD, UK.
authors: Martin
authors: M
authors: Forster
authors: Department of Economics, University of Verona, 37129 Verona, Italy. Electronic address: paolo.pertile@univr.it.
authors: Paolo
authors: P
authors: Pertile
authors: Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, SE-412 96, Gothenburg, Sweden; Advanced Analytics Centre, AstraZeneca R&D, SE-431 83 Mölndal, Sweden.
authors: Carl-Fredrik
authors: CF
authors: Burman
mesh_terms: D003132:Commerce; D003362:Cost-Benefit Analysis; D016527:Drug Costs; D004345:Drug Industry; D006801:Humans; D057766:Policy; D012106:Research / Q000191:economics*
keywords: Cost-effectiveness threshold; Optimal sample size; Pharmaceutical pricing and reimbursement; Rare diseases; Static and dynamic efficiency