Incorporating patients' characteristics in cost-effectiveness studies with clinical trial data: a flexible Bayesian approach.

Título inglés	Incorporating patients' characteristics in cost-effectiveness studies with clinical trial data: a flexible Bayesian approach.
Título español	Incorporación de las características de los pacientes a los estudios coste-eficacia con datos de ensayos clínicos: un enfoque bayesiano flexible.
Autor/es	Vázquez Polo, Francisco José ; Negrín Hernández, Miguel Angel
Organización	Dep. Mét. Cuantit. Fac. Cienc. Econ. Univ. Las Palmas, Las Palmas de Gran Canaria, España
Revista	1696-2281
Publicación	2004, 28 (1): 87-108, 46 Ref.
Tipo de documento	articulo
Idioma	Inglés
Resumen inglés	Most published research on the comparison between medical treatment options merely compares the results (effectiveness and cost) obtained for each treatment group. The present work proposes the incorporation of other patient characteristics into the analysis. Most of the studies carried out in this context assume normality of both costs and effectiveness. In practice, however, the data are not always distributed according to this assumption. Alternative models have to be developed. In this paper, we present a general model of cost-effectiveness, incorporating both binary effectiveness and skewed cost. In a practical application, we compare two highly active antiretroviral treatments applied to asymptomatic HIV patients. We propose a logit model when the effectiveness is measured depending on whether an initial purpose is achieved. For this model, the measure to compare treatments is the difference in the probability of success. Besides, the cost data usually present a right skewing. We propose the use of the log-transformation to carry out the regression model. The three models are fitted demonstrating the advantages of this modelling. The cost-effectiveness acceptability curve is used as a measure for decision-making.
Clasificación UNESCO	120900
Palabras clave español	Inferencia paramétrica ; Análisis bayesiano ; Regresión lineal ; Bioestadística ; Ensayo clínico
Código MathReviews	MR2076038
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