Bayesian Variable Selection in Cost-Effectiveness Analysis

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 illness...
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Autor*in:	Miguel A. Negrín [verfasserIn] Francisco J. Vázquez-Polo [verfasserIn] María Martel [verfasserIn] Elías Moreno [verfasserIn] Francisco J. Girón [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2010

Schlagwörter:	variable selection Bayesian analysis cost-effectiveness BIC Intrinsic Bayes Factor Fractional Bayes Factor subgroup analysis

Übergeordnetes Werk:	In: International Journal of Environmental Research and Public Health - MDPI AG, 2005, 7(2010), 4, Seite 1577-1596
Übergeordnetes Werk:	volume:7 ; year:2010 ; number:4 ; pages:1577-1596

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/ijerph7041577

Katalog-ID:	DOAJ059541342

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language	English
source	In International Journal of Environmental Research and Public Health 7(2010), 4, Seite 1577-1596 volume:7 year:2010 number:4 pages:1577-1596
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container_title	International Journal of Environmental Research and Public Health
authorswithroles_txt_mv	Miguel A. Negrín @@aut@@ Francisco J. Vázquez-Polo @@aut@@ María Martel @@aut@@ Elías Moreno @@aut@@ Francisco J. Girón @@aut@@
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author	Miguel A. Negrín
spellingShingle	Miguel A. Negrín misc variable selection misc Bayesian analysis misc cost-effectiveness misc BIC misc Intrinsic Bayes Factor misc Fractional Bayes Factor misc subgroup analysis misc Medicine misc R Bayesian Variable Selection in Cost-Effectiveness Analysis
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topic_title	Bayesian Variable Selection in Cost-Effectiveness Analysis variable selection Bayesian analysis cost-effectiveness BIC Intrinsic Bayes Factor Fractional Bayes Factor subgroup analysis
topic	misc variable selection misc Bayesian analysis misc cost-effectiveness misc BIC misc Intrinsic Bayes Factor misc Fractional Bayes Factor misc subgroup analysis misc Medicine misc R
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title	Bayesian Variable Selection in Cost-Effectiveness Analysis
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title_sort	bayesian variable selection in cost-effectiveness analysis
title_auth	Bayesian Variable Selection in Cost-Effectiveness Analysis
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.
abstractGer	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.
abstract_unstemmed	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.
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title_short	Bayesian Variable Selection in Cost-Effectiveness Analysis
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author2	Francisco J. Vázquez-Polo María Martel Elías Moreno Francisco J. Girón
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up_date	2024-07-03T23:55:19.087Z
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