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A bayesian model averaging approach with non-informative priors for cost-effectiveness analyses in health economics

  • Caterina Conigliani

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.

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

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Length: 22
Date of creation: Jul 2008
Date of revision:
Handle: RePEc:rtr:wpaper:0094
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  1. 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.
  2. 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.
  3. 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.
  4. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
  5. Anthony O'Hagan & John W. Stevens & Jacques Montmartin, 2000. "Inference for the Cost-Effectiveness Acceptability Curve and Cost-Effectiveness Ratio," PharmacoEconomics, Springer Healthcare | Adis, vol. 17(4), pages 339-349.
  6. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-39, November.
  7. 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.
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