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 .
Derivation of Utility Values from European Organization for Research and Treatment of Cancer Quality of Life-Core 30 Questionnaire Values in Lung Cancer
Introduction : Cancer clinical trials frequently incorporate quality of life ( QoL ) measures but rarely patient utility . Utility information is required for cost utility evaluations of novel cancer therapies . We assessed the feasibility of converting QoL data into utility scores using the European Organization for Research and Treatment of Cancer Quality of Life-Core  30 question naire ( EORTC QLQ-C30 ) and the EQ-5D in patients with non-small cell lung cancer ( NSCLC ) . Methods : Out patients with all different disease states of NSCLC attending a major Canadian cancer center completed the QLQ-C30 and EQ5D on a single visit . Results of the QLQ-C30 summary scores were mapped to predict EQ-5D utility scores using linear regression . Backward variable elimination using the Akaike Information Criterion was used to reduce the full model that included all QLQ-C30 summary scores to examine which QLQ-C30 dimensions best predict a patient 's utility score . To test the predictive power of the model , 10-fold cross-validation was used . Results : A total of 172 patients participated in the study . Median age of the sample was 66 years ( range , 32–85 years ) ; 46.5 % were men . The cross-validation estimate of mean utility score was 0.76 ( SD : 0.20 ) , which was the same as the actual mean utility score . Of the 15 QLQ-C30 dimensions , 4 functional dimensions ( physical , role , emotional , and social ) and the pain symptom dimension were predictive of patient utility scores . Conclusions : Our study demonstrates the feasibility of deriving utility scores from prospect i ve QoL data . Validation of the QLQ-C30 predictors found in this study could further the ability to estimate cost utility of therapies for economic evaluations
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 .