Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/47676
Title: Bayesian variable selection in cost-effectiveness analysis
Authors: Negrín Hernández, Miguel Ángel 
Vázquez Polo, Francisco José 
Martel Escobar, María Carmen 
Moreno, Elías
Girón, Francisco J.
UNESCO Clasification: 530204 Estadística económica
Keywords: Estadística bayesiana
Análisis coste-beneficio
Issue Date: 2010
Journal: International Journal of Environmental Research and Public Health 
Abstract: Linear regression models are often used to represent the cost and effectiveness of medical treatment. The covariates used may include sociodemographic variables, such as age, gender or race; clinical variables, such as initial health status, years of treatment or the existence of concomitant illnesses; and a binary variable indicating the treatment received. However, most studies estimate only one model, which usually includes all the covariates. This procedure ignores the question of uncertainty in model selection. In this paper, we examine four alternative Bayesian variable selection methods that have been proposed. In this analysis, we estimate the inclusion probability of each covariate in the real model conditional on the data. Variable selection can be useful for estimating incremental effectiveness and incremental cost, through Bayesian model averaging, as well as for subgroup analysis.
URI: http://hdl.handle.net/10553/47676
ISSN: 1660-4601
DOI: 10.3390/ijerph7041577
Source: International Journal of Environmental Research and Public Health,v. 7, p. 1577-1596
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