Eliciting expert opinion for cost-effectiveness analysis: a flexible family of prior distributions
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2099/8950
Tipus de documentArticle
Data publicació2009
EditorInstitut d'Estadística de Catalunya
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
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. However, 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. In a cost-effectiveness analysis, the parameters of interest are the mean efficacy and mean cost of each treatment. The most common prior structure for these two parameters is the bivariate normal structure. 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.
The model is validated using data of a real clinical trial. The posterior distributions have been simulated using Markov Chain Monte Carlo techniques.
CitacióMartel, María; Negrín, Miguel Angel; Vázquez Polo., Francisco J. Eliciting expert opinion for cost-effectiveness analysis: a flexible family of prior distributions. "SORT", 2009, vol. 33, núm. 2, p. 193-212.
ISSN1696-2281
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
eliciting.pdf | 320,5Kb | Visualitza/Obre |