IDEAS home Printed from https://ideas.repec.org/p/rtr/wpaper/0046.html
   My bibliography  Save this paper

A bayesian semi-parametric approach for cost-effectiveness analysis in health economics

Author

Listed:
  • 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 efficacy are available from a clinical trial, and we address it by means of the cost-effectiveness acceptability curve in the simple case where efficacy is measured as a binary outcome. We consider a Bayesian approach, and in recognising that cost data usually exhibit highly skew, heavy-tailed and, possibly multi-modal distributions, we introduce a model for costs composed of a piecewise constant density up to an unknown endpoint, and a generalised Pareto distribution for the remaining tail.

Suggested Citation

  • 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.
  • Handle: RePEc:rtr:wpaper:0046
    as

    Download full text from publisher

    File URL: http://dipeco.uniroma3.it/public/WP%2046%20Conigliani%20Tancredi%202005.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Caterina Conigliani, 2008. "A bayesian model averaging approach with non-informative priors for cost-effectiveness analyses in health economics," Departmental Working Papers of Economics - University 'Roma Tre' 0094, Department of Economics - University Roma Tre.
    2. Caterina Conigliani & Andrea Tancredi, 2006. "Comparing parametric and semi-parametric approaches for bayesian cost-effectiveness analyses in health economics," Departmental Working Papers of Economics - University 'Roma Tre' 0064, Department of Economics - University Roma Tre.
    3. 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.
    4. 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.

    More about this item

    Keywords

    Healthcare cost data; cost-effectiveness analysis; mixture models; semiparametric modelling.;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rtr:wpaper:0046. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Telephone for information (email available below). General contact details of provider: https://edirc.repec.org/data/dero3it.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.