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Int. J. Environ. Res. Public Health 2010, 7(4), 1577-1596; doi:10.3390/ijerph7041577

Bayesian Variable Selection in Cost-Effectiveness Analysis

1
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
2
Department of Statistics and Operation Research, University of Granada, Campus Fuentenueva, E-18071 Granada, Spain
3
Department of Statistics and Operation Research, University of Málaga, Campus de Teatinos, E-29071 Málaga, Spain
*
Author to whom correspondence should be addressed.
Received: 22 January 2010 / Revised: 28 March 2010 / Accepted: 29 March 2010 / Published: 6 April 2010
(This article belongs to the Special Issue Health Economics)
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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. View Full-Text
Keywords: variable selection; Bayesian analysis; cost-effectiveness; BIC; Intrinsic Bayes Factor; Fractional Bayes Factor; subgroup analysis variable selection; Bayesian analysis; cost-effectiveness; BIC; Intrinsic Bayes Factor; Fractional Bayes Factor; subgroup analysis
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Negrín, M.A.; Vázquez-Polo, F.J.; Martel, M.; Moreno, E.; Girón, F.J. Bayesian Variable Selection in Cost-Effectiveness Analysis. Int. J. Environ. Res. Public Health 2010, 7, 1577-1596.

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