Incorporating model uncertainty in cost-effectiveness analysis: a Bayesian model averaging approach.
J Health Econ
; 27(5): 1250-9, 2008 Sep.
Article
em En
| MEDLINE
| ID: mdl-18490067
ABSTRACT
Recently, several authors have proposed the use of linear regression models in 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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Avaliação de Processos e Resultados em Cuidados de Saúde
/
Teorema de Bayes
/
Modelos Econométricos
/
Análise Custo-Benefício
/
Incerteza
Tipo de estudo:
Clinical_trials
/
Health_economic_evaluation
/
Prognostic_studies
Limite:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
País/Região como assunto:
Europa
Idioma:
En
Ano de publicação:
2008
Tipo de documento:
Article