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 .
Mapping Between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and Five Multi-Attribute Utility Instruments (MAUIs)
Purpose Economic evaluation of health services commonly requires information regarding health-state utilities . Sometimes this information is not available but non-utility measures of quality of life may have been collected from which the required utilities can be estimated . This paper examines the possibility of mapping a non-utility-based outcome , the Sydney Asthma Quality of Life Question naire ( AQLQ-S ) , onto five multi-attribute utility instruments :  Assessment of Quality of Life 8 Dimensions ( AQoL-8D ) ,  EuroQoL 5 Dimensions 5-Level ( EQ-5D-5L ) , Health Utilities Index Mark 3 ( HUI3 ) , 15 Dimensions ( 15D ) , and the Short-Form 6 Dimensions ( SF-6D ) . Methods Data for 856 individuals with asthma were obtained from a large Multi-Instrument Comparison ( MIC ) survey . Four statistical techniques were employed to estimate utilities from the AQLQ-S. The predictive accuracy of 180 regression models was assessed using six criteria : mean absolute error ( MAE ) , root mean squared error ( RMSE ) , correlation , distribution of predicted utilities , distribution of residuals , and proportion of predictions with absolute errors < 0.0.5 . Validation of initial ‘ primary ’ models was carried out on a r and om sample of the MIC data . Results Best results were obtained with non-linear models that included a quadratic term for the AQLQ-S score along with demographic variables . The four statistical techniques predicted models that performed differently when assessed by the six criteria ; however , the best results , for both the estimation and validation sample s , were obtained using a generalised linear model ( GLM estimator ) . Conclusions It is possible to predict valid utilities from the AQLQ-S using regression methods . We recommend GLM models for this exercise
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 .