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 chronic liver disease questionnaire scores onto SF-6D utility values in patients with primary sclerosing cholangitis
Purpose The chronic liver disease question naire ( CLDQ ) is a frequently used liver-specific quality of life instrument , but it does not provide information on preference-adjusted health status , which is essential for cost-utility analysis . We aim ed to develop a mapping function deriving utilities from the CLDQ in primary sclerosing cholangitis ( PSC ) . Methods  Short form-6D ( SF-6D ) utilities  were calculated from SF-36 data collected in a recent prospect i ve study in which unselected patients with PSC also completed the CLDQ . Ordinary least squares ( OLS ) , generalized linear , median , and kernel regression analyses were employed to devise a mapping function predicting utilities . This was vali date d in three r and om sub sample s of the cohort and in a separate sample of PSC patients following liver transplantation .  Adjusted R2 and root-mean-square error ( RMSE ) as well as Pearson ’s r coefficients and mean absolute errors between predicted and observed values were used to determine model performance . Results Decompensated liver disease and fatigue , systemic symptoms , and emotional distress , assessed with the CLDQ , were related to worse SF-6D utilities . The final OLS prediction model explained 66.3 % of the variance in the derivation sample . Predicted and observed utilities were strongly correlated ( r = 0.807 , p < 0.001 ) , but the mean absolute error ( 0.0604 ) and adjusted RMSE ( 10.6 % ) were of intermediate size . Similar model characteristics were observed after employment of generalized linear and median regression models and at validation . Conclusions A model has been constructed , showing good validity predicting SF-6D utilities from CLDQ scores at the group level in PSC . Further testing is required to externally vali date the model
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 .