IDEAS home Printed from https://ideas.repec.org/a/wly/hlthec/v8y1999i3p203-211.html
   My bibliography  Save this article

Sample size and power issues in estimating incremental cost‐effectiveness ratios from clinical trials data

Author

Listed:
  • Andrew R. Willan
  • Bernie J. O'Brien

Abstract

It is becoming increasingly more common for a randomized controlled trial of a new therapy to include a prospective economic evaluation. The advantage of such trial‐based cost‐effectiveness is that conventional principles of statistical inference can be used to quantify uncertainty in the estimate of the incremental cost‐effectiveness ratio (ICER). Numerous articles in the recent literature have outlined and compared various approaches for determining confidence intervals for the ICER. In this paper we address the issue of power and sample size in trial‐based cost‐effectiveness analysis. Our approach is to determine the required sample size to ensure that the resulting confidence interval is narrow enough to distinguish between two regions in the cost‐effectiveness plane: one in which the new therapy is considered to be cost‐effective and one in which it is not. As a result, for a given sample size, the cost‐effectiveness plane is divided into two regions, separated by an ellipse centred at the origin, such that the sample size is adequate only if the truth lies on or outside the ellipse. Copyright © 1999 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:hlthec:v:8:y:1999:i:3:p:203-211
    DOI: 10.1002/(SICI)1099-1050(199905)8:3<203::AID-HEC413>3.0.CO;2-7
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/(SICI)1099-1050(199905)8:33.0.CO;2-7
    Download Restriction: no

    References listed on IDEAS

    as
    1. Daniel Polsky & Henry A. Glick & Richard Willke & Kevin Schulman, 1997. "Confidence Intervals for Cost–Effectiveness Ratios: A Comparison of Four Methods," Health Economics, John Wiley & Sons, Ltd., vol. 6(3), pages 243-252, May.
    2. 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.
    3. Alan Diener & Bernie O'Brien & Amiram Gafni, 1998. "Health care contingent valuation studies: a review and classification of the literature," Health Economics, John Wiley & Sons, Ltd., vol. 7(4), pages 313-326, June.
    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. Michaël Schwarzinger & Jean‐Louis Lanoë & Erik Nord & Isabelle Durand‐Zaleski, 2004. "Lack of multiplicative transitivity in person trade‐off responses," Health Economics, John Wiley & Sons, Ltd., vol. 13(2), pages 171-181, February.
    2. Joseph C. Gardiner & Marianne Huebner & James Jetton & Cathy J. Bradley, 2000. "Power and sample assessments for tests of hypotheses on cost‐effectiveness ratios," Health Economics, John Wiley & Sons, Ltd., vol. 9(3), pages 227-234, April.
    3. Walters, SJ & Brazier, JE, 2002. "Sample sizes for the SF-6D preference based measure of health from the SF-36: a practical guide," MPRA Paper 29742, University Library of Munich, Germany.
    4. Andrew Willan, 2011. "Sample Size Determination for Cost-Effectiveness Trials," PharmacoEconomics, Springer, vol. 29(11), pages 933-949, November.
    5. Henry Glick, 2011. "Sample Size and Power for Cost-Effectiveness Analysis (Part 1)," PharmacoEconomics, Springer, vol. 29(3), pages 189-198, March.
    6. Martin W. McIntosh & Scott D. Ramsey & Kristin Berry & Nicole Urban, 2001. "Parameter solicitation for planning cost effectiveness studies with dichotomous outcomes," Health Economics, John Wiley & Sons, Ltd., vol. 10(1), pages 53-66, January.
    7. A. Gafni & S. D. Walter & S. Birch & P. Sendi, 2008. "An opportunity cost approach to sample size calculation in cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 99-107, January.
    8. Gafni, Amiram & Birch, Stephen, 2006. "Incremental cost-effectiveness ratios (ICERs): The silence of the lambda," Social Science & Medicine, Elsevier, vol. 62(9), pages 2091-2100, 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:wly:hlthec:v:8:y:1999:i:3:p:203-211. 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: (Wiley Content Delivery). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/5749 .

    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 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.

    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.