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


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  • Andrew R. Willan

    (Department of Clinical Epidemiology and Biostatistics, McMaster University and Centre for Evaluation of Medicines, St. Joseph's Hospital, Canada)

  • Bernie J. O'Brien

    (Department of Clinical Epidemiology and Biostatistics, McMaster University and Centre for Evaluation of Medicines, St. Joseph's Hospital, Canada)

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    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.

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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

    Volume (Year): 8 (1999)
    Issue (Month): 3 ()
    Pages: 203-211

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    Handle: RePEc:wly:hlthec:v:8:y:1999:i:3:p:203-211

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    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.
    2. A Diener & B O'Brien & A Gafni, 1997. "Health Care Contingent Valuation Studies: A review and classification of the literature," Centre for Health Economics and Policy Analysis Working Paper Series 1997-07, Centre for Health Economics and Policy Analysis (CHEPA), McMaster University, Hamilton, Canada.
    3. 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.
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    Cited by:
    1. Andrew Willan, 2011. "Sample Size Determination for Cost-Effectiveness Trials," PharmacoEconomics, Springer, vol. 29(11), pages 933-949, November.
    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.
    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. 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.
    5. 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.
    6. Micha�l Schwarzinger & Jean-Louis Lano� & 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.
    7. Henry Glick, 2011. "Sample Size and Power for Cost-Effectiveness Analysis (Part 1)," PharmacoEconomics, Springer, vol. 29(3), pages 189-198, March.


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