A portuguese value set for the SF-6D

Ferreira, Lara Noronha; Ferreira, Pedro L.; Pereira, Luis Nobre; Brazier, John; Rowen, Donna

http://hdl.handle.net/10400.1/1286

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Authors

Ferreira, Lara Noronha

Abstract(s)

Objectives: The SF-6D is a preference-based measure of health derived from the SF-36 that can be used for cost-effectiveness analysis using cost-per-quality adjusted life-year analysis. This study seeks to estimate a system weight for the SF-6D for Portugal and to compare the results with the UK system weights. Methods: A sample of 55 health states defined by the SF-6D has been valued by a representative random sample of the Portuguese population, stratified by sex and age (n = 140), using the Standard Gamble (SG). Several models are estimated at both the individual and aggregate levels for predicting health-state valuations. Models with main effects, with interaction effects and with the constant forced to unity are presented. Random effects (RE) models are estimated using generalized least squares (GLS) regressions. Generalized estimation equations (GEE) are used to estimate RE models with the constant forced to unity. Estimations at the individual level were performed using 630 health-state valuations. Alternative functional forms are considered to account for the skewed distribution of health-state valuations.Results: The models are analyzed in terms of their coefficients, overall fit, and the ability for predicting the SG-values. The RE models estimated using GLS and through GEE produce significant coefficients, which are robust across model specification. However, there are concerns regarding some inconsistent estimates, and so parsimonious consistent models were estimated. There is evidence of under prediction in some states assigned to poor health. The results are consistent with the UK results. Conclusion: The models estimated provide preference-based quality of life weights for the Portuguese population when health status data have been collected using the SF-36. Although the sample was randomly drowned findings should be treated with caution, given the small sample size, even knowing that they have been estimated at the individual level.