This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

A Bayesian Model Averaging Approach With Non-Informative Priors For Cost-Effectiveness Analyses In Health Economics

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
Caterina Conigliani

Additional information is available for the following registered author(s):

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 in the particular case of weak prior informations about the unknown parameters of the different models involved in the procedure. The main consequence of this assumption is that the marginal densities required by Bayesian model averaging are undetermined. However in accordance with the theory of partial Bayes factors and in particular of fractional Bayes factors, we suggest replacing each marginal density with a ratio of integrals, that can be efficiently computed via Path Sampling. The results in terms of cost-effectiveness are compared with those obtained with a semi-parametric approach that does not require any assumption about the distribution of costs.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://host.uniroma3.it/dipartimenti/economia/pdf/wp94.pdf
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Department of Economics - University Roma Tre in its series Departmental Working Papers of Economics - University 'Roma Tre' with number 0094.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length: 22
Date of creation: Jul 2008
Date of revision:
Handle: RePEc:rtr:wpaper:0094

Contact details of provider:
Postal: Via Silvio d'Amico 77, - 00145 Rome Italy
Phone: +39 06 57114612
Fax: +39 06 57114771
Email:
Web page: http://host.uniroma3.it/dipartimenti/economia/it/
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (Telephone for information).

Related research
Keywords: Bayesian model averaging; Cost data; Health economics; MCMC; Non-informative priors;

Other versions of this item:

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods

This paper has been announced in the following NEP Reports:

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November. [Downloadable!] (restricted)
  2. 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. [Downloadable!]
  3. 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.
  4. 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.
  5. A. O’Hagan, 1997. "Properties of intrinsic and fractional Bayes factors," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 6(1), pages 101-118, June. [Downloadable!] (restricted)
Full references

Statistics
Access and download statistics

Did you know? Each page is provided with a technical contact, in case something is not right with the supplied information. See under "publisher info".

This page was last updated on 2009-11-27.


This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.