title: Expected net present value of sample information: from burden to investment.
abstract: The Expected Value of Information Framework has been proposed as a method for identifying when health care technologies should be immediately reimbursed and when any reimbursement should be withheld while awaiting more evidence. This framework assesses the value of obtaining additional evidence to inform a current reimbursement decision. This represents the burden of not having the additional evidence at the time of the decision. However, when deciding whether to reimburse now or await more evidence, decision makers need to know the value of investing in more research to inform a future decision. Assessing this value requires consideration of research costs, research time, and what happens to patients while the research is undertaken and after completion. The investigators describe a development of the calculation of the expected value of sample information that assesses the value of investing in further research, including an only-in-research strategy and an only-with-research strategy.
authors.affiliation: Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK (PSH, RE, CM)|Clinical Trials Research Unit, University of Leeds, Leeds, UK (PSH, WG)|St James Institute of Oncology, Leeds Teaching Hospitals NHS Trust (PSH)
authors.forename: Peter S
authors.initials: PS
authors.lastname: Hall
authors.affiliation: Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK (PSH, RE, CM)
authors.forename: Richard
authors.initials: R
authors.lastname: Edlin
authors.affiliation: Department of Mathematics, University of York, York, UK (SK)
authors.forename: Samer
authors.initials: S
authors.lastname: Kharroubi
authors.affiliation: Clinical Trials Research Unit, University of Leeds, Leeds, UK (PSH, WG)
authors.forename: Walter
authors.initials: W
authors.lastname: Gregory
authors.affiliation: Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds, Leeds, UK (PSH, RE, CM)
authors.forename: Christopher
authors.initials: C
authors.lastname: McCabe
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003657:Decision Making; D017048:Health Care Costs; D006285:Health Planning / Q000191:economics* / Q000706:statistics & numerical data; D006751:Hospital Information Systems / Q000191:economics*; D006801:Humans; D007182:Income / Q000706:statistics & numerical data*; D008490:Medical Informatics / Q000191:economics* / Q000706:statistics & numerical data; D017146:Models, Organizational; D008962:Models, Theoretical; D009010:Monte Carlo Method; D019057:Quality-Adjusted Life Years; D006113:United Kingdom