An opportunity cost approach to sample size calculation in cost-effectiveness analysis

A. Gafni, S. D. Walter, S. Birch, P. Sendi

    Research output: Contribution to journalArticlepeer-review


    The inclusion of economic evaluations as part of clinical trials has led to concerns about the adequacy of trial sample size to support such analysis. The analytical tool of cost-effectiveness analysis is the incremental cost-effectiveness ratio (ICER), which is compared with a threshold value (λ) as a method to determine the efficiency of a health-care intervention. Accordingly, many of the methods suggested to calculating the sample size requirements for the economic component of clinical trials are based on the properties of the ICER. However, use of the ICER and a threshold value as a basis for determining efficiency has been shown to be inconsistent with the economic concept of opportunity cost. As a result, the validity of the ICER-based approaches to sample size calculations can be challenged. Alternative methods for determining improvements in efficiency have been presented in the literature that does not depend upon ICER values. In this paper, we develop an opportunity cost approach to calculating sample size for economic evaluations alongside clinical trials, and illustrate the approach using a numerical example. We compare the sample size requirement of the opportunity cost method with the ICER threshold method. In general, either method may yield the larger required sample size. However, the opportunity cost approach, although simple to use, has additional data requirements. We believe that the additional data requirements represent a small price to pay for being able to perform an analysis consistent with both concept of opportunity cost and the problem faced by decision makers. Copyright © 2007 John Wiley & Sons, Ltd.
    Original languageEnglish
    Pages (from-to)99-107
    Number of pages8
    JournalHealth Economics
    Issue number1
    Publication statusPublished - Jan 2008


    • Cost-effectiveness analysis
    • Decision rules
    • Sample size calculations


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