Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/69835
Title: Subgroup optimal decisions in cost–effectiveness analysis
Authors: Moreno, E.
Vázquez Polo, Francisco José 
Negrín Hernández, Miguel Ángel 
Martel Escobar, María Carmen 
UNESCO Clasification: 530204 Estadística económica
Keywords: Bayesian Variable Selection
Cost–Effectiveness
Optimal Treatments
Estadística bayesiana
Optimización, et al
Issue Date: 2019
Journal: Advances in Intelligent Systems and Computing 
Conference: 15th International Conference on Distributed Computing and Artificial Intelligence, DCAI 2018 
Abstract: In cost–effectiveness analysis (CEA) of medical treatments the optimal treatment is chosen using an statistical model of the cost and effectiveness of the treatments, and data from patients under the treatments. Sometimes these data also include values of certain deterministic covariates of the patients which usually have valuable clinical information that would be incorporated into the statistical treatment selection procedure. This paper discusses the usual statistical models to undertake this task, and the main statistical problems it involves. The consequence is that the optimal treatments are now given for patient subgroups instead of for the patient population, where the subgroup are defined by those patients that share some covariate values, for instance age, gender, etc. Some of the covariates are non necessarily influential, as typically occurs in regression analysis, and an statistical variable selection procedure is called for. A Bayesian variable selection procedure is presented, and optimal treatments for subgroups defined by the selected covariates are then found.
URI: http://hdl.handle.net/10553/69835
ISBN: 9783319996974
ISSN: 2194-5357
DOI: 10.1007/978-3-319-99698-1_8
Source: Advances in Intelligent Systems and Computing[ISSN 2194-5357],v. 805, p. 67-74
Appears in Collections:Actas de congresos
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