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Bayesian cost‐effectiveness analysis with two measures of effectiveness: the cost‐effectiveness acceptability plane

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  • Miguel A. Negrín
  • Francisco J. Vázquez‐Polo

Abstract

Cost‐effectiveness analysis (CEA) compares the costs and outcomes of two or more technologies. However, there is no consensus about which measure of effectiveness should be used in each analysis. Clinical researchers have to select an appropriate outcome for their purpose, and this choice can have dramatic consequences on the conclusions of their analysis. In this paper we present a Bayesian cost‐effectiveness framework to carry out CEA when more than one measure is considered. In particular, we analyse the case in which two measures of effectiveness, one binary and the other continuous, are considered. Decision‐making measures, such as the incremental cost‐effectiveness ratio, incremental net‐benefit and cost‐effectiveness acceptability curves, are used to compare costs and one measure of outcome. We propose an extension of cost‐acceptability curves, namely the cost‐effectiveness acceptability plane, as a suitable measure for decision taking. The models were validated using data from two clinical trials. In the first one, we compared four highly active antiretroviral treatments applied to asymptomatic HIV patients. As measures of effectiveness, we considered the percentage of patients with undetectable levels of viral load, and changes in quality of life, measured according to EuroQol. In the second clinical trial we compared three methadone maintenance programmes for opioid‐addicted patients. In this case, the measures of effectiveness considered were quality of life, according to the Nottingham Health Profile, and adherence to the treatment, measured as the percentage of patients who participated in the whole treatment programme. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Miguel A. Negrín & Francisco J. Vázquez‐Polo, 2006. "Bayesian cost‐effectiveness analysis with two measures of effectiveness: the cost‐effectiveness acceptability plane," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 363-372, April.
    • Handle: RePEc:wly:hlthec:v:15:y:2006:i:4:p:363-372
    DOI: 10.1002/hec.1056
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    References listed on IDEAS

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    1. Anthony O’Hagan & John W. Stevens, 2001. "Bayesian Assessment of Sample Size for Clinical Trials of Cost-Effectiveness," Medical Decision Making, , vol. 21(3), pages 219-230, May.
    2. Anthony O'Hagan & John W. Stevens, 2001. "A framework for cost‐effectiveness analysis from clinical trial data," Health Economics, John Wiley & Sons, Ltd., vol. 10(4), pages 303-315, June.
    3. Jakob Bjørner & Hans Keiding, 2004. "Cost‐effectiveness with multiple outcomes," Health Economics, John Wiley & Sons, Ltd., vol. 13(12), pages 1181-1190, December.
    4. Gerald Richardson & Andrea Manca, 2004. "Calculation of quality adjusted life years in the published literature: a review of methodology and transparency," Health Economics, John Wiley & Sons, Ltd., vol. 13(12), pages 1203-1210, December.
    5. Elisabeth Fenwick & Karl Claxton & Mark Sculpher, 2001. "Representing uncertainty: the role of cost‐effectiveness acceptability curves," Health Economics, John Wiley & Sons, Ltd., vol. 10(8), pages 779-787, December.
    6. Andrew H. Briggs, 1999. "A Bayesian approach to stochastic cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 257-261, May.
    7. Daniel F. Heitjan & Alan J. Moskowitz & William Whang, 1999. "Bayesian estimation of cost‐effectiveness ratios from clinical trials," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 191-201, May.
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    1. Navarro Espigares, José Luis & Hernández Torres, Elisa, 2006. "Health Technologies Assessment: Analysing The Role Of Uncertainty/Evaluación De Tecnologías Sanitarias: Análisis Del Papel De La Incertidumbre," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 24, pages 731-754, Diciembre.

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