Conclusions The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions
Background Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values ( HSUVs ) from data obtained from another health-related quality of life ( HRQoL ) measure .
There have been several method ological developments in this area since a previous review up to 2007 .
A Portuguese value set for the SF-6D.
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 r and om sample of the Portuguese population , stratified by sex and age ( n = 140 ) , using the St and ard 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 . R and om 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 r and omly drowned findings should be treated with caution , given the small sample size , even knowing that they have been estimated at the individual level
Conclusions The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions
Background Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values ( HSUVs ) from data obtained from another health-related quality of life ( HRQoL ) measure .
There have been several method ological developments in this area since a previous review up to 2007 .