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Clinical importance, statistical significance and the assessment of economic and quality‐of‐life outcomes

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  • Michael Drummond
  • Bernie O'Brienm

Abstract

The assessment of economic and quality‐of‐life outcomes of health care interventions is moving into a new era, with such assessments increasingly being made within the context of controlled clinical trials. Traditionally the measurement of many variables in economic evaluations, particularly costs, has been deterministic. In the context of clinical trials the measurement of variables is stochastic, with the standard principles of statistical inference being applied to analyse differences between treatments in terms of effectiveness. Economists participating in clinical research are therefore being called upon to specify the sample size for the economic component of the evaluation and to undertake statistical tests for differences in cost or cost‐effectiveness. This paper discusses the current methodological issues surrounding stochastic measurement in clinical trials, discusses the additional issues raised by the assessment of economic and quality‐of‐life outcomes and specifies the challenges facing economists if they are to answer the questions now being posed about economic analysis by statisticians and clinical researchers. It is concluded that application of the standard principles of statistical inference to economic data is not straightforward and will require value judgements to be made about statistical significance and economic importance, which may differ from those already made in purely clinical studies.

Suggested Citation

  • Michael Drummond & Bernie O'Brienm, 1993. "Clinical importance, statistical significance and the assessment of economic and quality‐of‐life outcomes," Health Economics, John Wiley & Sons, Ltd., vol. 2(3), pages 205-212, October.
    • Handle: RePEc:wly:hlthec:v:2:y:1993:i:3:p:205-212
    DOI: 10.1002/hec.4730020303
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    References listed on IDEAS

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    1. Maynard, Alan, 1991. "Developing the Health Care Market," Economic Journal, Royal Economic Society, vol. 101(408), pages 1277-1286, September.
    2. Drummond, Michael F. & Davies, Linda M. & Ferris, Frederick L., 1992. "Assessing the costs and benefits of medical research: The diabetic retinopathy study," Social Science & Medicine, Elsevier, vol. 34(9), pages 973-981, May.
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    Cited by:

    1. 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.
    2. Maureen P. M. H. Rutten‐van Mölken & Eddy K. A. Van & René C. J. A. van Vliet, 1994. "Statistical analysis of cost outcomes in a randomized controlled clinical trial," Health Economics, John Wiley & Sons, Ltd., vol. 3(5), pages 333-345, September.
    3. Magnus Tambour & Niklas Zethraeus, 1998. "Bootstrap confidence intervals for cost‐effectiveness ratios: some simulation results," Health Economics, John Wiley & Sons, Ltd., vol. 7(2), pages 143-147, March.
    4. Anthony O’Hagan & John Stevens & Jacques Montmartin, 2000. "Inference for the Cost-Effectiveness Acceptability Curve and Cost-Effectiveness Ratio," PharmacoEconomics, Springer, vol. 17(4), pages 339-349, April.

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