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Methods to Assess Cost-Effectiveness and Value of Further Research When Data Are Sparse

Author

Listed:
  • Marta O. Soares
  • Jo C. Dumville
  • Rebecca L. Ashby
  • Cynthia P. Iglesias
  • Laura Bojke
  • Una Adderley
  • Elizabeth McGinnis
  • Nikki Stubbs
  • David J. Torgerson
  • Karl Claxton
  • Nicky Cullum

Abstract

Health care resources are scarce, and decisions have to be made about how to allocate funds. Often, these decisions are based on sparse or imperfect evidence. One such example is negative-pressure wound therapy (NPWT), which is a widely used treatment for severe pressure ulcers; however, there is currently no robust evidence that it is effective or cost-effective. This work considers the decision to adopt NPWT given a range of alternative treatments, using a decision analytic modeling approach. Literature searches were conducted to identify existing evidence on model parameters. Given the limited evidence base, a second source of evidence, beliefs elicited from experts, was used. Judgments from experts on relevant (uncertain) quantities were obtained through a formal elicitation exercise. Additionally, data derived from a pilot trial were also used to inform the model. The 3 sources of evidence were collated, and the impact of each on cost-effectiveness was evaluated. An analysis of the value of further information indicated that a randomized controlled trial may be worthwhile in reducing decision uncertainty, where from a set of alternative designs, a 3-arm trial with longer follow-up was estimated to be the most efficient. The analyses presented demonstrate how allocation decisions about medical technologies can be explicitly informed when data are sparse and how this kind of analyses can be used to guide future research prioritization, not only indicating whether further research is worthwhile but what type of research is needed and how it should be designed.

Suggested Citation

  • Marta O. Soares & Jo C. Dumville & Rebecca L. Ashby & Cynthia P. Iglesias & Laura Bojke & Una Adderley & Elizabeth McGinnis & Nikki Stubbs & David J. Torgerson & Karl Claxton & Nicky Cullum, 2013. "Methods to Assess Cost-Effectiveness and Value of Further Research When Data Are Sparse," Medical Decision Making, , vol. 33(3), pages 415-436, April.
  • Handle: RePEc:sae:medema:v:33:y:2013:i:3:p:415-436
    DOI: 10.1177/0272989X12451058
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    References listed on IDEAS

    as
    1. Ouchi, Fumika, 2004. "A literature review on the use of expert opinion in probabilistic risk analysis," Policy Research Working Paper Series 3201, The World Bank.
    2. Robert T. Clemen & Robert L. Winkler, 1999. "Combining Probability Distributions From Experts in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 187-203, April.
    3. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
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