Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/48770
Title: Incorporating model uncertainty in cost-effectiveness analysis: A Bayesian model averaging approach
Authors: Negrín, Miguel A. 
Vázquez-Polo, Francisco José 
Keywords: Clinical-Trial Data
Variable Selection
Prediction
Framework
Window, et al
Issue Date: 2008
Publisher: 0167-6296
Journal: Journal of Health Economics 
Abstract: Recently, several authors have proposed the use of linear regression models ill cost-effectiveness analysis. In this paper, by modelling costs and Outcomes using patient and Health Centre covariates, we seek to identify the part of the Cost Or Outcome difference that is not attributable to the treatment itself, but to the patients' condition or to characteristics of the Centres. Selection of the covariates to be included as predictors of effectiveness and Cost is usually assumed by the researcher. This behaviour ignores the uncertainty associated with model selection and leads to underestimation of the uncertainty about quantities of interest. We propose the use of Bayesian model averaging as a mechanism to account for Such uncertainty about the model. Data from a clinical trial are used to analyze the effect of incorporating model uncertainty, by comparing two highly active antiretroviral treatments applied to asymptomatic HIV patients. The joint posterior density of incremental effectiveness and cost and cost-effectiveness acceptability Curves are proposed as decision-making measures. (c) 2008 Elsevier B.V. All rights reserved.
URI: http://hdl.handle.net/10553/48770
ISSN: 0167-6296
DOI: 10.1016/j.jhealeco.2008.03.005
Source: Journal of Health Economics[ISSN 0167-6296],v. 27, p. 1250-1259
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