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Impact of small study bias on cost-effectiveness acceptability curves and value of information analyses

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  • Dirk Müller

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  • Eleanor Pullenayegum
  • Afschin Gandjour

Abstract

It is well known that small, randomized, controlled trials (RCTs) have limited validity. When comparing the results of meta-analyses with those of later large trials or with those of large trials removed from the meta-analyses, discrepancies were reported. This paper addresses two issues: (1) how measures of the uncertainty in cost-effectiveness, i.e., cost-effectiveness acceptability curves (CEACs), and the expected value of perfect information (EVPI) are affected by the limited validity of small trials and (2) how to deal with this bias. To this end, the paper adopts a Bayesian approach. Using empirical estimates for the validity of small RCTs compared to larger RCTs, the probability of cost-effectiveness drops by almost 10 %, while the EVPI is three times higher. In conclusion, traditional CEACs and EVPI analyses based on (small) RCTs may need careful appraisal. Ignoring prior evidence on the validity of small-size trials leads to an underestimation of uncertainty in cost-effectiveness. For future economic analyses, it is important to incorporate aspects of uncertainty which are caused by flawed data on effectiveness . Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Dirk Müller & Eleanor Pullenayegum & Afschin Gandjour, 2015. "Impact of small study bias on cost-effectiveness acceptability curves and value of information analyses," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(2), pages 219-223, March.
  • Handle: RePEc:spr:eujhec:v:16:y:2015:i:2:p:219-223
    DOI: 10.1007/s10198-014-0607-3
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    File URL: http://hdl.handle.net/10.1007/s10198-014-0607-3
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    References listed on IDEAS

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    1. Hoch, Jeffrey S. & Blume, Jeffrey D., 2008. "Measuring and illustrating statistical evidence in a cost-effectiveness analysis," Journal of Health Economics, Elsevier, vol. 27(2), pages 476-495, March.
    2. Michał Jakubczyk & Bogumił Kamiński, 2010. "Cost‐effectiveness acceptability curves – caveats quantified," Health Economics, John Wiley & Sons, Ltd., vol. 19(8), pages 955-963, August.
    3. Mickael Löthgren & Niklas Zethraeus, 2000. "Definition, interpretation and calculation of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 9(7), pages 623-630, October.
    4. 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.
    5. Bas Groot Koerkamp & M. G. Myriam Hunink & Theo Stijnen & Milton C. Weinstein, 2006. "Identifying key parameters in cost‐effectiveness analysis using value of information: a comparison of methods," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 383-392, April.
    6. Andrew H. Briggs, 1999. "A Bayesian approach to stochastic cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 257-261, May.
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    More about this item

    Keywords

    Cost-effectiveness analyses; Value of information; Uncertainty; I1;

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health

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