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 utility scores from a disease-specific quality-of-life measure in bariatric surgery patients.
OBJECTIVES To develop algorithms for a conversion of disease-specific quality -of-life into health state values for morbidly obese patients before or after bariatric surgery . METHODS A total of 893 patients were enrolled in a prospect i ve cross-sectional multicenter study . In addition to demographic and clinical data , health-related quality -of-life ( HRQoL ) data were collected using the disease-specific Moorehead-Ardelt II question naire ( MA-II ) and two generic question naires , the EuroQoL-5D ( EQ-5D ) and the Short Form-6D ( SF-6D ) . Multiple regression models were constructed to predict EQ-5D- and SF-6D-based utility values from MA-II scores and additional demographic variables . RESULTS The mean body mass index was 39.4 , and 591 patients ( 66 % ) had already undergone surgery . The average EQ-5D and SF-6D scores were 0.830 and 0.699 . The MA-IIwas correlated to both utility measures ( Spearman 's r = 0.677 and 0.741 ) . Goodness-of-fit was highest ( R(2 ) = 0.55 in the validation sample ) for the following item-based transformation algorithm : utility ( MA-II-based ) = 0.4293 + ( 0.0336 x MA1 ) + ( 0.0071 x MA2 ) + ( 0.0053 x MA3 ) + ( 0.0107 x MA4 ) + ( 0.0001 x MA5 ) . This EQ-5D-based mapping algorithm outperformed a similar SF-6D-based algorithm in terms of mean absolute percentage error ( P = 0.045 ) . CONCLUSIONS Because the mapping algorithm estimated utilities with only minor errors , it appears to be a valid method for calculating health state values in cost-utility analyses . The algorithm will help to define the role of bariatric surgery in morbid obesity
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 .