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Please use this identifier to cite or link to this item: http://hdl.handle.net/10481/32287

Title: Bayesian variable selection in cost-effectiveness analysis
Authors: Negrín, Miguel A.
Vázquez-Polo, Francisco J.
Martel-Escobar, María del Carmen
Moreno, Elías
Girón, Francisco J.
Issue Date: 2010
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.
Sponsorship: This research was partially supported by grants SEJ-02814 (Junta de Andalucía, Spain), SEJ2007-65200 and SEJ2006-12685 (Ministerio de Educación y Ciencia, Spain) and ECO2009-14152 (Ministerio de Ciencia e Innovación, Spain).
Publisher: MDPI
Keywords: Variable selection
Bayesian analysis
Cost-effectiveness
BIC
Intrinsic Bayes factor
Fractional Bayes Factor
Subgroup analysis
URI: http://hdl.handle.net/10481/32287
ISSN: 1660-4601
Citation: Negrín, M.A.; et al. Bayesian variable selection in cost-effectiveness analysis. International Journal of Environmental Research and Public Health, 7: 1577-1596 (2010). [http://hdl.handle.net/10481/32287]
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