Expected Net Present Value of Sample Information: from burden to investment
AbstractThe Expected Value of Information Framework has been proposed as a method for identifying when health care technologies should be reimbursed and when reimbursement should be withheld awaiting more evidence. The standard framework assesses the value of having additional evidence available to inform a current reimbursement decision. This can be thought of as the burden of not having the additional evidence available at the time of the decision. However, the information that decision makers need to decide whether to reimburse now or await more evidence is the value of investing in the creation of the new evidence to inform a future decision. Assessing the value requires the analysis to incorporate the costs of the research, the time it will take for the research to report and what happens to patients whilst the research is undertaken and once it has reported. In this paper we describe a development of the calculation of the expected value of sample information that assesses the value of investing in further research using (a) an only-in-research strategy; and (b) an only-with-research strategy.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds in its series Working Papers with number 1101.
Length: 25 pages
Date of creation: 2011
Date of revision:
Publication status: Published in Medical Decision Making, May-June 2012, Vol 32 no. 3, pages E11-E21
Contact details of provider:
Phone: Charles Thackrah Building, 101 Clarendon Road, LEEDS LS9 2LJ
Fax: +44 (0) 113 343 3470
Web page: http://medhealth.leeds.ac.uk/auhe
More information through EDIRC
expected value of information; research design; economic evaluation; decision theory;
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Jeremy E. Oakley & Anthony O'Hagan, 2004. "Probabilistic sensitivity analysis of complex models: a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(3), pages 751-769.
- J. Kirsch & A. McGuire, 2000. "Establishing health state valuations for disease specific states: an example from heart disease," Health Economics, John Wiley & Sons, Ltd., vol. 9(2), pages 149-158.
- Simon Eckermann & Andrew R. Willan, 2007. "Expected value of information and decision making in HTA," Health Economics, John Wiley & Sons, Ltd., vol. 16(2), pages 195-209.
- Claxton, Karl, 1999. "The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies," Journal of Health Economics, Elsevier, vol. 18(3), pages 341-364, June.
- Christopher McCabe & Richard Edlin & Peter Hall, 2012.
"Navigating time and uncertainty in health technology appraisal: would a map help?,"
1201, Academic Unit of Health Economics, Leeds Institute of Health Sciences, University of Leeds.
- Christopher McCabe & Richard Edlin & Peter Hall, 2013. "Navigating Time and Uncertainty in Health Technology Appraisal: Would a Map Help?," PharmacoEconomics, Springer, vol. 31(9), pages 731-737, September.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Thomas Veale).
If references are entirely missing, you can add them using this form.