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