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Comparing parametric and semi-parametric approaches for bayesian cost-effectiveness analyses in health economics

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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 heavytailed 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 its posterior expectation, that can be obtained as a weighted mean of the 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. 1 Introduction

Suggested Citation

  • 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.
  • Handle: RePEc:rtr:wpaper:0064
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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, vol. 65(2), pages 391-404.
    2. 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.
    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. 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.
    5. 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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    More about this item

    Keywords

    Healthcare cost data; cost-effectiveness analysis; mixture models; Bayesian model averaging;

    JEL classification:

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

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