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 .
The development of a model for translation of the Neck Disability Index to utility scores for cost-utility analysis in cervical disorders.
BACKGROUND CONTEXT The Neck Disability Index ( NDI ) is a commonly used disease-specific instrument for cervical spine disorders with good responsiveness and psychometric properties compared with general health status measures . However , NDI scores are unitless and do not have an intrinsic value that is comparable to other health status measures , and these scores have limited value in cost-utility analysis . The translation of disease-specific measures to Short Form-6 Dimensions ( SF-6D ) utility scores may be useful in cost-utility analysis . PURPOSE The purpose of this study is to present a model for translating the NDI to SF-6D utility scores , permitting the use of NDI scores in the cost-utility analysis of cervical disorders . STUDY DESIGN / SETTING A secondary analysis of a multicenter prospect i ve clinical trial of the Synthes ProDisc-C ( Synthes , West Chester , PA , USA ) was performed . PATIENT SAMPLE Patients included were r and omized to receive either a total disc arthroplasty or anterior cervical discectomy and fusion for treatment of symptomatic cervical disc disease involving one vertebral level between C3 and C7 . All subjects completed NDI and 36-Item Short Form Health Survey ( SF-36 ) self- assessment s at preoperative and postoperative follow-ups of 6 weeks , 3 , 6 , 12 , 18 , and 24 months . OUTCOME MEASURES The NDI is a vali date d and widely used self-reported question naire design ed to assess patient-determined disability result ing from neck pain , including pain level and effects on activities of daily living . The SF-6D is a preference-based health state classification system derived from six health dimensions of the SF-36 self-reported question naire , including the domains of physical functioning , role limitation , social functioning , bodily pain , mental health , and vitality . METHODS The collected data points were divided into two cohorts : one for model formation and one for the assessment of model validity . SF-36 scores were converted to SF-6D utilities via three previously published methods . Correlation analyses and linear regression modeling between SF-6D and NDI created the models for translating scores . For validation , Spearman and Pearson correlations were calculated between the observed and predicted SF-6D utilities , and prediction errors were calculated . RESULTS Four hundred thirty patients with 2,137 time points were used for creation and validation of the model . Pearson and Spearman correlation coefficients between the NDI and the SF-6D derived from each conversion method were found to be between -0.8255 and -0.8504 ( p<.01 ) . R(2 ) values ranged from 0.68 to 0.71 and root mean squared error ( RMSE ) from 0.092 to 0.084 . Correlations between estimated and observed SF-6D scores ranged from 0.8325 to 0.8372 ( p<.01 ) . The mean prediction error was less than 0.006 , with st and ard deviation ( SD ) between 0.082 and 0.093 . DISCUSSION Correlations between NDI and SF-6D utility scores are strong and statistically significant . The model has a large R(2 ) and small RMSE . The prediction models produce a small mean prediction error , but the SD of the prediction errors is large . High correlations between NDI and SF-6D permit these models to be used to calculate overall utilities , changes in utilities , and quality -adjusted life-years for large data sample s. However , the relatively large observed prediction error SDs may limit the accuracy of translation of individual data points or small sample sizes
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 .