Publication: Inverse probability weighted estimation of social tariffs: an illustration using the SF-6D value sets
Authors
Méndez Martínez, Ildefonso ; Abellán Perpiñán, José María ; Sánchez Martínez, Fernando I. ; Martínez Pérez, Jorge E.
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Publisher
Elsevier
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DOI
https://doi.org/10.1016/j.jhealeco.2011.07.013
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info:eu-repo/semantics/article
Description
© 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
Abstract
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
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Citation
Journal of Health Economics, 2011, Vol. 30, N. 6, pp. 1280-1292
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1-ene-2999
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