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A FULLY BAYESIAN COST–EFFECTIVENESS ANALYSIS USING CONDITIONALLY SPECIFIED PRIOR DISTRIBUTIONS pp. 227-241 $100.00
Authors:  (M. Martel, M.A. Negrin, F.J. Vazquez-Polo, Dept. of Quantitative Methods, Univ. of Las Palmas de G.C., Campus de Tafira, Las Palmas de Gran Canaria, Spain)
Abstract:
The Bayesian approach to statistics has been growing rapidly in popularity as an
alternative to the classical approach in the economic evaluation of health technologies,
due to the significant benefits it affords. One of the most important advantages of
Bayesian methods is their incorporation of prior information. Thus, use is made of a
greater amount of information, and so stronger results are obtained than with frequentist
methods.
In a cost-effectiveness analysis, we relate the costs and effectiveness of the two
technologies being compared, the parameters of interest being the mean effectiveness
and mean cost of each. The most common prior structure for these two parameters
is the bivariate normal structure. Since Stevens and O’Hagan (2002) showed that
the elicitation of a prior distribution on the parameters of interest plays a crucial role
in a Bayesian cost-effectiveness analysis, relatively few papers have addressed this
issue, although Leal et al. (2007) recently presented a computer-based model to elicit
uncertainty on parameters.
In this paper we study the use of a more general (and flexible) family of prior
distributions for the parameters. In particular, we assume that the conditional densities
of the parameters are all normal. This structure allows us to incorporate a large range
of prior information. The bivariate normal distribution is included as a particular case
of the conditional prior structure. 


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A FULLY BAYESIAN COST–EFFECTIVENESS ANALYSIS USING CONDITIONALLY SPECIFIED PRIOR DISTRIBUTIONS pp. 227-241