title: Bayesian variable selection in cost-effectiveness analysis.
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.
authors.affiliation: Department of Quantitative Methods, University of Las Palmas de Gran Canaria, Faculty of Economics, Campus de Tafira, E-35017 Las Palmas de G.C. Canary Islands, Spain. mnegrin@dmc.ulpgc.es
authors.forename: Miguel A
authors.initials: MA
authors.lastname: Negrín
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Vázquez-Polo
authors.forename: María
authors.initials: M
authors.lastname: Martel
authors.forename: Elías
authors.initials: E
authors.lastname: Moreno
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Girón
mesh_terms: D001499:Bayes Theorem; D003362:Cost-Benefit Analysis