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Sample Size and Power for Cost-Effectiveness Analysis (Part 2)

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  • Henry Glick

Abstract

Sample size and power for cost-effectiveness analysis depend on assumptions about the difference in cost and effect, the standard deviations of cost and effect, the correlation of the difference in cost and effect, the α and β errors and maximum willingness to pay (W). The first seven of these parameters share much in common in their effect on sample size and power for cost-effectiveness analysis, including that each is associated with a single pattern of power. W, on the other hand, is unique in that, when plotted for positive values, we can potentially observe any of six patterns of power associated with positive values of W. In addition, as W approaches ∞, power need be neither monotonically increasing nor decreasing and it can be multimodal. In this article, the relationship betweenWand sample size and power is explained. Copyright Adis Data Information BV 2011

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  • Henry Glick, 2011. "Sample Size and Power for Cost-Effectiveness Analysis (Part 2)," PharmacoEconomics, Springer, vol. 29(4), pages 287-296, April.
    • Handle: RePEc:spr:pharme:v:29:y:2011:i:4:p:287-296
    DOI: 10.2165/11585080-000000000-00000
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    1. Nancy Devlin & David Parkin, 2004. "Does NICE have a cost‐effectiveness threshold and what other factors influence its decisions? A binary choice analysis," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 437-452, May.
    2. Henry Glick, 2011. "Sample Size and Power for Cost-Effectiveness Analysis (Part 1)," PharmacoEconomics, Springer, vol. 29(3), pages 189-198, March.
    3. Glick, Henry A & Doshi, Jalpa A & Sonnad, Seema S & Polsky, Daniel, 2007. "Economic Evaluation in Clinical Trials," OUP Catalogue, Oxford University Press, number 9780198529972, December.
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