IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v33y2013i4p472-489.html
   My bibliography  Save this article

The Role of Value-of-Information Analysis in a Health Care Research Priority Setting

Author

Listed:
  • Isaac Corro Ramos
  • Maureen P. M. H. Rutten-van Mölken
  • Maiwenn J. Al

Abstract

Background . The Dutch reimbursement procedure for expensive drugs requires the submission of a baseline cost-effectiveness (CE) analysis and a research plan for the period of temporary reimbursement to estimate the real-life cost-effectiveness after 4 years. The Dutch guidelines recommend a value-of-information analysis to identify the critical parameters to be studied in such an outcome study. Objectives . Identify situations where sensitivity analyses are sufficient to establish the need for additional data collection and priority setting. Methods . We used a hypothetical Markov model with 3 groups of parameters. We performed deterministic and probabilistic sensitivity analyses (PSA) and analyzed the expected value of partial perfect information (EVPPI), for different configurations of input parameters and a range of threshold incremental cost-effectiveness ratios (λ). We introduced a multivariate (deterministic) sensitivity analysis and a partial PSA. Results . Deterministic, partial PSA, and EVPPI analyses came to the same ranking of priorities for future research in most cases, irrespective of the place of the results on the CE plane. Rankings differed only when the statistical metrics that we calculated for each method were close together. Conclusions . When a clear ranking can be established, all methods lead to the same priority setting. If there is no clear ranking, we regard the parameters as equally important. Priority setting for future research depends on λ and the location of results on the CE plane. The EVPPI is needed to estimate the value of doing additional research, but to prioritize parameters for further research, extensive (partial probabilistic) sensitivity analyses and expected value of perfect information are often sufficient.

Suggested Citation

  • Isaac Corro Ramos & Maureen P. M. H. Rutten-van Mölken & Maiwenn J. Al, 2013. "The Role of Value-of-Information Analysis in a Health Care Research Priority Setting," Medical Decision Making, , vol. 33(4), pages 472-489, May.
  • Handle: RePEc:sae:medema:v:33:y:2013:i:4:p:472-489
    DOI: 10.1177/0272989X12468616
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X12468616
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X12468616?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Andrew Briggs & Mark Sculpher & Martin Buxton, 1994. "Uncertainty in the economic evaluation of health care technologies: The role of sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 3(2), pages 95-104, March.
    2. Oakley, Jeremy E. & Brennan, Alan & Tappenden, Paul & Chilcott, Jim, 2010. "Simulation sample sizes for Monte Carlo partial EVPI calculations," Journal of Health Economics, Elsevier, vol. 29(3), pages 468-477, May.
    3. 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.
    4. Andrew Briggs & Mark Sculpher, 1995. "Sensitivity analysis in economic evaluation: A review of published studies," Health Economics, John Wiley & Sons, Ltd., vol. 4(5), pages 355-371, September.
    5. Kennedy, Marc C. & Anderson, Clive W. & Conti, Stefano & O’Hagan, Anthony, 2006. "Case studies in Gaussian process modelling of computer codes," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1301-1309.
    6. 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, August.
    7. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. John Hutton, 2012. "‘Health Economics’ and the evolution of economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 21(1), pages 13-18, January.
    2. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023.
    3. Petropoulos, G. & Wooster, M.J. & Carlson, T.N. & Kennedy, M.C. & Scholze, M., 2009. "A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation," Ecological Modelling, Elsevier, vol. 220(19), pages 2427-2440.
    4. McKenna, Claire & Chalabi, Zaid & Epstein, David & Claxton, Karl, 2010. "Budgetary policies and available actions: A generalisation of decision rules for allocation and research decisions," Journal of Health Economics, Elsevier, vol. 29(1), pages 170-181, January.
    5. Antony M. Overstall & David C. Woods, 2016. "Multivariate emulation of computer simulators: model selection and diagnostics with application to a humanitarian relief model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 65(4), pages 483-505, August.
    6. Storlie, Curtis B. & Helton, Jon C., 2008. "Multiple predictor smoothing methods for sensitivity analysis: Example results," Reliability Engineering and System Safety, Elsevier, vol. 93(1), pages 55-77.
    7. Laura Bojke & Karl Claxton & Stephen Palmer & Mark Sculpher, 2006. "Defining and characterising structural uncertainty in decision analytic models," Working Papers 009cherp, Centre for Health Economics, University of York.
    8. Nicky J. Welton & Jason J. Madan & Deborah M. Caldwell & Tim J. Peters & Anthony E. Ades, 2014. "Expected Value of Sample Information for Multi-Arm Cluster Randomized Trials with Binary Outcomes," Medical Decision Making, , vol. 34(3), pages 352-365, April.
    9. K Cooper & S C Brailsford & R Davies, 2007. "Choice of modelling technique for evaluating health care interventions," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(2), pages 168-176, February.
    10. Nicholas Graves & Katie Page & Elizabeth Martin & David Brain & Lisa Hall & Megan Campbell & Naomi Fulop & Nerina Jimmeison & Katherine White & David Paterson & Adrian G Barnett, 2016. "Cost-Effectiveness of a National Initiative to Improve Hand Hygiene Compliance Using the Outcome of Healthcare Associated Staphylococcus aureus Bacteraemia," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    11. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023, October.
    12. Nancy Wolff & Thomas W. Helminiak, 1996. "Nonsampling measurement error in administrative data: Implications for economic evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 5(6), pages 501-512, November.
    13. Kobelt, G., 2013. "Health Economics: An Introduction to Economic Evaluation," Monographs, Office of Health Economics, number 000004.
    14. Plischke, Elmar & Borgonovo, Emanuele & Smith, Curtis L., 2013. "Global sensitivity measures from given data," European Journal of Operational Research, Elsevier, vol. 226(3), pages 536-550.
    15. Mohan V. Bala & Gary A. Zarkin & Josephine Mauskopf, 2008. "Presenting results of probabilistic sensitivity analysis: the incremental benefit curve," Health Economics, John Wiley & Sons, Ltd., vol. 17(3), pages 435-440, March.
    16. Karl Claxton & Stephen Palmer & Louise Longworth & Laura Bojke & Susan Griffin & Claire McKenna & Marta Soares & Eldon Spackman & Jihee Youn, 2011. "Uncertainty, evidence and irrecoverable costs: Informing approval, pricing and research decisions for health technologies," Working Papers 069cherp, Centre for Health Economics, University of York.
    17. John W. Stevens, 2018. "Using Evidence from Randomised Controlled Trials in Economic Models: What Information is Relevant and is There a Minimum Amount of Sample Data Required to Make Decisions?," PharmacoEconomics, Springer, vol. 36(10), pages 1135-1141, October.
    18. Trevor A. Sheldon, 1996. "Problems of using modelling in the economic evaluation of health care," Health Economics, John Wiley & Sons, Ltd., vol. 5(1), pages 1-11, January.
    19. N. J. Welton & A. E. Ades & D. M. Caldwell & T. J. Peters, 2008. "Research prioritization based on expected value of partial perfect information: a case‐study on interventions to increase uptake of breast cancer screening," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 807-841, October.
    20. Marta O Soares & L Canto e Castro, 2010. "Simulation or cohort models? Continuous time simulation and discretized Markov models to estimate cost-effectiveness," Working Papers 056cherp, Centre for Health Economics, University of York.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:33:y:2013:i:4:p:472-489. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.