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A Bayesian model averaging approach for cost-effectiveness analyses

Author

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  • Caterina Conigliani

    (Dipartimento di Economia, Università Roma Tre, Roma, Italy)

  • Andrea Tancredi

    (Dipartimento di Studi Geoeconomici, Linguistici, Statistici e Storici per l'Analisi Regionale, Università di Roma La Sapienza, Roma, Italy)

Abstract

We consider the problem of assessing new and existing technologies for their cost-effectiveness in the case where data on both costs and effects are available from a clinical trial, and we address it by means of the cost-effectiveness acceptability curve. The main difficulty in these analyses is that cost data usually exhibit highly skew and heavy-tailed distributions so that it can be extremely difficult to produce realistic probabilistic models for the underlying population distribution, and in particular to model accurately the tail of the distribution, which is highly influential in estimating the population mean. Here, in order to integrate the uncertainty about the model into the analysis of cost data and into cost-effectiveness analyses, we consider an approach based on Bayesian model averaging: instead of choosing a single parametric model, we specify a set of plausible models for costs and estimate the mean cost with a weighted mean of its posterior expectations under each model, with weights given by the posterior model probabilities. The results are compared with those obtained with a semi-parametric approach that does not require any assumption about the distribution of costs. Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Caterina Conigliani & Andrea Tancredi, 2009. "A Bayesian model averaging approach for cost-effectiveness analyses," Health Economics, John Wiley & Sons, Ltd., vol. 18(7), pages 807-821.
  • Handle: RePEc:wly:hlthec:v:18:y:2009:i:7:p:807-821 DOI: 10.1002/hec.1404
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    References listed on IDEAS

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    1. Richard Royall & Tsung-Shan Tsou, 2003. "Interpreting statistical evidence by using imperfect models: robust adjusted likelihood functions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, pages 391-404.
    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.
    3. Andrew Briggs & Richard Nixon & Simon Dixon & Simon Thompson, 2005. "Parametric modelling of cost data: some simulation evidence," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 421-428.
    4. West, Kenneth D & McCracken, Michael W, 1998. "Regression-Based Tests of Predictive Ability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 817-840, November.
    5. Maiwenn J. Al & Ben A. Van Hout, 2000. "A Bayesian approach to economic analyses of clinical trials: the case of stenting versus balloon angioplasty," Health Economics, John Wiley & Sons, Ltd., vol. 9(7), pages 599-609.
    6. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, pages 270-281.
    7. Caterina Conigliani & Andrea Tancredi, 2005. "A bayesian semi-parametric approach for cost-effectiveness analysis in health economics," Departmental Working Papers of Economics - University 'Roma Tre' 0046, Department of Economics - University Roma Tre.
    8. Caterina Conigliani & Andrea Tancredi, 2003. "Semi-parametric modelling for costs of helt care technologies," Departmental Working Papers of Economics - University 'Roma Tre' 0034, Department of Economics - University Roma Tre.
    9. Anthony O'Hagan & John W. Stevens, 2003. "Assessing and comparing costs: how robust are the bootstrap and methods based on asymptotic normality?," Health Economics, John Wiley & Sons, Ltd., vol. 12(1), pages 33-49.
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    Cited by:

    1. Anna-Liesa Lange & Philipp Otto, 2016. "Bayes’sche Statistik in der Dienstleistungsforschung
      [Bayesian statistics in service research]
      ," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 10(4), pages 247-267, December.

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