|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
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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