IDEAS home Printed from https://ideas.repec.org/a/spr/pharme/v29y2011i11p933-949.html
   My bibliography  Save this article

Sample Size Determination for Cost-Effectiveness Trials

Author

Listed:
  • Andrew Willan

Abstract

Methods for determining sample size requirements for cost-effectiveness studies are reviewed and illustrated. Traditional methods based on tests of hypothesis and power arguments are given for the incremental costeffectiveness ratio and incremental net benefit (INB). In addition, a full Bayesian approach using decision theory to determine optimal sample size is given for INB. The full Bayesian approach, based on the value of information, is proposed in reaction to concerns that traditional methods rely on arbitrarily chosen error probabilities and an ill-defined notion of the smallest clinically important difference. Furthermore, the results of cost-effectiveness studies are used for decision making (e.g. should a new intervention be adopted or the old one retained), and employing decision theory, which permits optimal use of current information and the optimal design of new studies, provides a more consistent approach. Copyright Springer International Publishing AG 2011

Suggested Citation

  • Andrew Willan, 2011. "Sample Size Determination for Cost-Effectiveness Trials," PharmacoEconomics, Springer, vol. 29(11), pages 933-949, November.
    • Handle: RePEc:spr:pharme:v:29:y:2011:i:11:p:933-949
    DOI: 10.2165/11587130-000000000-00000
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.2165/11587130-000000000-00000
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.2165/11587130-000000000-00000?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tambour, Magnus & Zethraeus, Niklas & Johannesson, Magnus, 1997. "A Note on Confidence Intervals in Cost-Effectiveness Analysis," SSE/EFI Working Paper Series in Economics and Finance 181, Stockholm School of Economics.
    2. Simon Eckermann & Andrew R. Willan, 2009. "Globally optimal trial design for local decision making," Health Economics, John Wiley & Sons, Ltd., vol. 18(2), pages 203-216, February.
    3. Anthony O’Hagan & John W. Stevens, 2001. "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness," Medical Decision Making, , vol. 21(3), pages 219-230, May.
    4. Andrew Briggs & Richard Nixon & Simon Dixon & Simon Thompson, 2005. "Parametric modelling of cost data: some simulation evidence," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 421-428, April.
    5. Simon Eckermann & Andrew R. Willan, 2008. "The Option Value of Delay in Health Technology Assessment," Medical Decision Making, , vol. 28(3), pages 300-305, May.
    6. Andre Ament & Rob Baltussen, 1997. "The Interpretation of results of economic evaluation: explicating the value of health," Health Economics, John Wiley & Sons, Ltd., vol. 6(6), pages 625-635, November.
    7. Andrew H. Briggs & David E. Wonderling & Christopher Z. Mooney, 1997. "Pulling cost‐effectiveness analysis up by its bootstraps: A non‐parametric approach to confidence interval estimation," Health Economics, John Wiley & Sons, Ltd., vol. 6(4), pages 327-340, July.
    8. 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, February.
    9. Andrew R. Willan & Simon Eckermann, 2010. "Optimal clinical trial design using value of information methods with imperfect implementation," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 549-561, May.
    10. Claxton, K. & Thompson, K. M., 2001. "A dynamic programming approach to the efficient design of clinical trials," Journal of Health Economics, Elsevier, vol. 20(5), pages 797-822, September.
    11. 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.
    12. Andrew R. Willan & Bernie J. O'Brien, 1999. "Sample size and power issues in estimating incremental cost‐effectiveness ratios from clinical trials data," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 203-211, May.
    13. Yi Cheng, 2003. "Choosing sample size for a clinical trial using decision analysis," Biometrika, Biometrika Trust, vol. 90(4), pages 923-936, December.
    14. Andrew Briggs & Paul Fenn, 1998. "Confidence intervals or surfaces? Uncertainty on the cost‐effectiveness plane," Health Economics, John Wiley & Sons, Ltd., vol. 7(8), pages 723-740, December.
    15. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel P Beavers & James D Stamey, 2018. "Bayesian sample size determination for cost-effectiveness studies with censored data," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-16, January.

    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. Simon Eckermann & Andrew R. Willan, 2009. "Globally optimal trial design for local decision making," Health Economics, John Wiley & Sons, Ltd., vol. 18(2), pages 203-216, February.
    2. Andrew Willan & Simon Eckermann, 2012. "Value of Information and Pricing New Healthcare Interventions," PharmacoEconomics, Springer, vol. 30(6), pages 447-459, June.
    3. Andrew R. Willan & Simon Eckermann, 2012. "Accounting For Between‐Study Variation In Incremental Net Benefit In Value Of Information Methodology," Health Economics, John Wiley & Sons, Ltd., vol. 21(10), pages 1183-1195, October.
    4. Andrew R. Willan & Simon Eckermann, 2010. "Optimal clinical trial design using value of information methods with imperfect implementation," Health Economics, John Wiley & Sons, Ltd., vol. 19(5), pages 549-561, May.
    5. Simon Eckermann & Andrew Willan, 2011. "Presenting Evidence and Summary Measures to Best Inform Societal Decisions When Comparing Multiple Strategies," PharmacoEconomics, Springer, vol. 29(7), pages 563-577, July.
    6. Andrew R. Willan & Andrew H. Briggs & Jeffrey S. Hoch, 2004. "Regression methods for covariate adjustment and subgroup analysis for non‐censored cost‐effectiveness data," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 461-475, May.
    7. Samer A. Kharroubi & Alan Brennan & Mark Strong, 2011. "Estimating Expected Value of Sample Information for Incomplete Data Models Using Bayesian Approximation," Medical Decision Making, , vol. 31(6), pages 839-852, November.
    8. K. Claxton & P. J. Neumannn & S. S. Araki & M. C. Weinstein, "undated". "Bayesian Value-of-Information Analysis: An Application to a Policy Model of Alzheimer's Disease," Discussion Papers 00/39, Department of Economics, University of York.
    9. Andrew R. Willan & Matthew E. Kowgier, 2008. "Cost‐effectiveness analysis of a multinational RCT with a binary measure of effectiveness and an interacting covariate," Health Economics, John Wiley & Sons, Ltd., vol. 17(7), pages 777-791, July.
    10. Manca, A & Austin, P. C, 2008. "Using propensity score methods to analyse individual patient-level cost-effectiveness data from observational studies," Health, Econometrics and Data Group (HEDG) Working Papers 08/20, HEDG, c/o Department of Economics, University of York.
    11. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225, November.
    12. Penny Breeze & Alan Brennan, 2015. "Valuing Trial Designs from a Pharmaceutical Perspective Using Value‐Based Pricing," Health Economics, John Wiley & Sons, Ltd., vol. 24(11), pages 1468-1482, November.
    13. Alan Brennan & Samer A. Kharroubi, 2007. "Expected value of sample information for Weibull survival data," Health Economics, John Wiley & Sons, Ltd., vol. 16(11), pages 1205-1225.
    14. 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.
    15. Elizabeth Fenwick & Karl Claxton & Mark Sculpher & Andrew Briggs, 2000. "Improving the efficiency and relevance of health technology assessent: the role of iterative decision analytic modelling," Working Papers 179chedp, Centre for Health Economics, University of York.
    16. Lauren E. Cipriano & Thomas A. Weber, 2018. "Population-level intervention and information collection in dynamic healthcare policy," Health Care Management Science, Springer, vol. 21(4), pages 604-631, December.
    17. Niklas Zethraeus & Magnus Johannesson & Bengt Jönsson & Mickael Löthgren & Magnus Tambour, 2003. "Advantages of Using the Net-Benefit Approach for Analysing Uncertainty in Economic Evaluation Studies," PharmacoEconomics, Springer, vol. 21(1), pages 39-48, January.
    18. Stefano Conti & Karl Claxton, 2008. "Dimensions of design space: a decision-theoretic approach to optimal research design," Working Papers 038cherp, Centre for Health Economics, University of York.
    19. Richard M. Nixon & David Wonderling & Richard D. Grieve, 2010. "Non‐parametric methods for cost‐effectiveness analysis: the central limit theorem and the bootstrap compared," Health Economics, John Wiley & Sons, Ltd., vol. 19(3), pages 316-333, March.
    20. 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.

    More about this item

    Statistics

    Access and download statistics

    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:spr:pharme:v:29:y:2011:i:11:p:933-949. See general information about how to correct material in RePEc.

    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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.