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A Bayesian model averaging approach for cost‐effectiveness analyses

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  • Caterina Conigliani
  • Andrea Tancredi

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, July.
  • Handle: RePEc:wly:hlthec:v:18:y:2009:i:7:p:807-821
    DOI: 10.1002/hec.1404
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    3. Richard Grieve & Richard Nixon & Simon G. Thompson, 2010. "Bayesian Hierarchical Models for Cost-Effectiveness Analyses that Use Data from Cluster Randomized Trials," Medical Decision Making, , vol. 30(2), pages 163-175, March.
    4. Mohamed El Alili & Johanna M. van Dongen & Jonas L. Esser & Martijn W. Heymans & Maurits W. van Tulder & Judith E. Bosmans, 2022. "A scoping review of statistical methods for trial‐based economic evaluations: The current state of play," Health Economics, John Wiley & Sons, Ltd., vol. 31(12), pages 2680-2699, December.
    5. Christopher H. Jackson & Laura Bojke & Simon G. Thompson & Karl Claxton & Linda D. Sharples, 2011. "A Framework for Addressing Structural Uncertainty in Decision Models," Medical Decision Making, , vol. 31(4), pages 662-674, July.
    6. Miguel A. Negrín & Francisco J. Vázquez-Polo & María Martel & Elías Moreno & Francisco J. Girón, 2010. "Bayesian Variable Selection in Cost-Effectiveness Analysis," IJERPH, MDPI, vol. 7(4), pages 1-20, April.
    7. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, August.

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