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 .
Predicting health-related quality of life (EQ-5D-5 L) and capability wellbeing (ICECAP-A) in the context of opiate dependence using routine clinical outcome measures: CORE-OM, LDQ and TOP
Background Economic evaluation normally requires information to be collected on outcome improvement using utility values . This is often not collected during the treatment of substance use disorders making cost-effectiveness evaluations of therapy difficult . One potential solution is the use of mapping to generate utility values from clinical measures . This study develops and evaluates mapping algorithms that could be used to predict the EuroQol-5D ( EQ-5D-5 L ) and the ICEpop CAPability measure for Adults ( ICECAP-A ) from the three commonly used clinical measures ; the CORE-OM , the LDQ and the TOP measures . Methods Models were estimated using pilot trial data of heroin users in opiate substitution treatment . In the trial the EQ-5D-5 L , ICECAP-A , CORE-OM , LDQ and TOP were administered at baseline , three and twelve month time intervals . Mapping was conducted using estimation and validation data sets . The normal estimation data set , which comprised of baseline sample data , used ordinary least squares ( OLS ) and tobit regression methods . Data from the baseline and three month time periods were combined to create a pooled estimation data set . Cluster and mixed regression methods were used to map from this data set . Predictive accuracy of the models was assessed using the root mean square error ( RMSE ) and the mean absolute error ( MAE ) . Algorithms were vali date d using sample data from the follow-up time periods . Results Mapping algorithms can be used to predict the ICECAP-A and the EQ-5D-5 L in the context of opiate dependence . Although both measures can be predicted , the ICECAP-A was better predicted by the clinical measures . There were no advantages of pooling the data . There were 6 chosen mapping algorithms , which had MAE scores ranging from 0.100 to 0.138 and RMSE scores ranging from 0.134 to 0.178 . Conclusion It is possible to predict the scores of the ICECAP-A and the EQ-5D-5 L with the use of mapping . In the context of opiate dependence , these algorithms provide the possibility of generating utility values from clinical measures and thus enabling economic evaluation of alternative therapy options . Trial registration IS RCT N22608399 . Date of registration : 27/04/2012 . Date of first r and omisation : 14/08/2012
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 .