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 SF-6D Utility Scores From the Neck Disability Index and Numeric Rating Scales for Neck and Arm Pain
Study Design . Cross-sectional cohort . Objective . This study aims to provide an algorithm to estimate Short Form-6D ( SF-6D ) utilities using data from the Neck Disability Index ( NDI ) , neck pain , and arm pain scores . Summary of Background Data . Although cost-utility analysis is increasingly used to provide information about the relative value of alternative interventions , health state values or utilities are rarely available from clinical trial data . The Neck Disability Index ( NDI ) and numerical rating scales for neck and arm pain are widely used diseasespecific measures in patients with cervical degenerative disorders . The purpose of this study is to provide an algorithm to allow estimation of SF-6D utilities using data from the NDI , and numerical rating scales for neck and arm pain . Methods . SF-36 , NDI , neck and arm pain rating scale scores were prospect ively collected before surgery , at 12 and 24 months after surgery in 2080 patients undergoing cervical fusion for degenerative disorders . SF-6D utilities were computed , and Spearman correlation coefficients were calculated for paired observations from multiple time points between NDI , neck and arm pain scores , and SF-6D utility scores . SF-6D scores were estimated from the NDI , neck and arm pain scores were estimated using a linear regression model . Using a separate , independent data set of 396 patients in which NDI scores were available , SF-6D was estimated for each subject and compared to their actual SF-6D . Results . The mean age for those in the development sample was 50.4 ± 11.0 years and 33 % were male . In the validation sample , the mean age was 53.1 ± 9.9 years and 35 % were male . Correlations between the SF-6D and the NDI , neck and arm pain scores were statistically significant ( P < 0.0001 ) with correlation coefficients of 0.82 , 0.62 , and 0.50 , respectively . The regression equation using NDI aloneto predict SF-6D had an R2 of 0.66 and a root mean square error of 0.056 . In the validation analysis , there was no statistically significant difference ( P = 0.961 ) between actual mean SF-6D ( 0.49 ± 0.08 ) and the estimated mean SF-6D score ( 0.49 ± 0.08 ) , using the NDI regression model . Conclusion . This regression-based algorithm may be a useful tool to predict SF-6D scores in studies of cervical degenerative disease that have collected NDI but not utility scores
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 .