Por favor, use este identificador para citar o enlazar este ítem: https://doi.org/10.1016/j.jhealeco.2011.07.013

Título: Inverse probability weighted estimation of social tariffs: an illustration using the SF-6D value sets
Fecha de publicación: dic-2011
Editorial: Elsevier
Cita bibliográfica: Journal of Health Economics, 2011, Vol. 30, N. 6, pp. 1280-1292
ISSN: Print: 0167-6296
Electronic: 1879-1646
Palabras clave: Inverse probability weighting
Propensity score
Preference based health measure
SF 6D
Resumen: This paper presents a novel approach to model health state valuations using inverse probability weighting techniques. Our approach makes no assumption on the distribution of health state values, accommodates covariates in a flexible way, eschews parametric assumptions on the relationship between the outcome and the covariates, allows for an undetermined amount of heterogeneity in the estimates and it formally tests and corrects for sample selection biases. The proposed model is semi-parametrically estimated and it is illustrated with health state valuation data collected for Spain using the SF-6D descriptive system. Estimation results indicate that the standard regression model underestimates the utility loss that the Spanish general population assigns to departures from full health, particularly so for severe departures
Autor/es principal/es: Méndez Martínez, Ildefonso
Abellán Perpiñán, José María
Sánchez Martínez, Fernando I.
Martínez Pérez, Jorge E.
Versión del editor: https://www.sciencedirect.com/science/article/pii/S016762961100107X?via%3Dihub
URI: http://hdl.handle.net/10201/146983
DOI: https://doi.org/10.1016/j.jhealeco.2011.07.013
Tipo de documento: info:eu-repo/semantics/article
Número páginas / Extensión: 13
Derechos: info:eu-repo/semantics/embargoedAccess
Descripción: © 2011 Elsevier B.V. This document is the Published version of a Published Work that appeared in final form in Journal of Health Economics. To access the final edited and published work see https://doi.org/10.1016/j.jhealeco.2011.07.013
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