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Sample size calculation in economic evaluations

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  • Maiwenn J. Al
  • Ben A. Van Hout
  • Bowine C. Michel
  • Frans F.H. Rutten

Abstract

A simulation method is presented for sample size calculation in economic evaluations. As input the method requires: the expected difference and variance of costs and effects, their correlation, the significance level (α) and the power of the testing method and the maximum acceptable ratio of incremental effectiveness to incremental costs. The method is illustrated with data from two trials. The first compares primary coronary angioplasty with streptokinase in the treatment of acute myocardial infarction, in the second trial, lansoprazole is compared with omeprazole in the treatment of reflux oesophagitis. These case studies show how the various parameters influence the sample size. Given the large number of parameters that have to be specified in advance, the lack of knowledge about costs and their standard deviation, and the difficulty of specifying the maximum acceptable ratio of incremental effectiveness to incremental costs, the conclusion of the study is that from a technical point of view it is possible to perform a sample size calculation for an economic evaluation, but one should wonder how useful it is. © 1998 John Wiley & Sons, Ltd.

Suggested Citation

  • Maiwenn J. Al & Ben A. Van Hout & Bowine C. Michel & Frans F.H. Rutten, 1998. "Sample size calculation in economic evaluations," Health Economics, John Wiley & Sons, Ltd., vol. 7(4), pages 327-335, June.
    • Handle: RePEc:wly:hlthec:v:7:y:1998:i:4:p:327-335
    DOI: 10.1002/(SICI)1099-1050(199806)7:4<327::AID-HEC342>3.0.CO;2-U
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    References listed on IDEAS

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    1. 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.
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    1. 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.
    2. Chiu, Singa Wang & Wang, Shan-Ling & Chiu, Yuan-Shyi Peter, 2007. "Determining the optimal run time for EPQ model with scrap, rework, and stochastic breakdowns," European Journal of Operational Research, Elsevier, vol. 180(2), pages 664-676, July.
    3. Manuel Gomes & Richard Grieve & Richard Nixon & W. J. Edmunds, 2012. "Statistical Methods for Cost-Effectiveness Analyses That Use Data from Cluster Randomized Trials," Medical Decision Making, , vol. 32(1), pages 209-220, January.
    4. 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.
    5. Meltzer, David, 2001. "Addressing uncertainty in medical cost-effectiveness analysis: Implications of expected utility maximization for methods to perform sensitivity analysis and the use of cost-effectiveness analysis to s," Journal of Health Economics, Elsevier, vol. 20(1), pages 109-129, January.

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