RESULTS ( 1 ) EQ-5D was valid and responsive for skin conditions and most cancers ; in vision , its performance varied according to aetiology ; and performance was poor for hearing impairments .
The HUI3 performed well for hearing and vision disorders .
It also performed well in cancers although evidence was limited and there was no evidence in skin conditions .
Response mapping using all the domain scores was the best performing model for the EORTC QLQ-C30 .
In an exploratory analysis , a limited dependent variable mixture model performed better than an equivalent linear model .
3 ) The exploratory valuation study found that bolt-on items for vision , hearing and tiredness had a significant impact on values of the health states , but the direction and magnitude of differences depended on the severity of the health state .
The vision bolt-on item had a statistically significant impact on EQ-5D health state values and a full valuation model was estimated .
EQ-5D performs well in studies of cancer and skin conditions .
Mapping techniques provide a solution to predict EQ-5D values where EQ-5D has not been administered .
For conditions where EQ-5D was found to be inappropriate , including some vision disorders and for hearing , bolt-ons provide a promising solution .
BACKGROUND The National Institute for Health and Care Excellence recommends the use of generic preference-based measures ( GPBMs ) of health for its Health Technology Assessment s ( HTAs ) .
However , these data may not be available or appropriate for all health conditions .
OBJECTIVES To determine whether GPBMs are appropriate for some key conditions and to explore alternative methods of utility estimation when data from GPBMs are unavailable or inappropriate .
( 2 ) Potential modelling approaches to ' map ' EQ-5D values from condition-specific and clinical measures of health [ European Organisation for Research and Treatment of Cancer Quality -of-life Question naire Core 30 ( EORTC QLQ-C30 ) and Functional Assessment of Cancer Therapy - General Scale ( FACT-G ) ] are compared for predictive ability and goodness of fit using two separate data sets . (
Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments.
OBJECTIVES To estimate models , via ordinary least squares regression , for predicting Euro Qol 5D ( EQ-5D ) , Short Form 6D ( SF-6D ) , and 15D utilities from scale scores of the European Organization for Research and Treatment of Cancer Quality of Life Question naire ( EORTC QLQ-C30 ) . METHODS Forty-eight gastric cancer patients , split up into equal subgroups by age , sex , and chemotherapy scheme , were interviewed , and the survey included the QLQ-C30 , SF-36 , EQ-5D , and 15D instruments , along with sociodemographic and clinical data . Model predictive ability and explanatory power were assessed by root mean square error ( RMSE ) and adjusted R(2 ) values , respectively . Pearson 's r between predicted and reported utility indices was compared . Three r and om sub sample s , half in size the initial sample , were created and used for " external " validation of the modeling equations . RESULTS Explanatory power was high , with adjusted R(2 ) reaching 0.909 , 0.833 , and 0.611 for 15D , SF-6D , and EQ-5D , respectively . After normalization of RMSE to the range of possible values , the prediction errors were 12.0 , 5.4 , and 5.6 % for EQ-5D , SF-6D , and 15D , respectively . The estimation equations produced a range of utility scores similar to those achievable by the st and ard scoring algorithms . Predicted and reported indices from the validation sample s were comparable thus confirming the previous results . CONCLUSIONS Evidence on the ability of QLQ-C30 scale scores to validly predict 15D and SF-6D utilities , and to a lesser extent , EQ-5D , has been provided . The modeling equations must be tried in future studies with larger and more diverse sample s to confirm their appropriateness for estimating quality -adjusted life-year in cancer-patient trials including only the QLQ-C30
RESULTS ( 1 ) EQ-5D was valid and responsive for skin conditions and most cancers ; in vision , its performance varied according to aetiology ; and performance was poor for hearing impairments .
The HUI3 performed well for hearing and vision disorders .
It also performed well in cancers although evidence was limited and there was no evidence in skin conditions .
Response mapping using all the domain scores was the best performing model for the EORTC QLQ-C30 .
In an exploratory analysis , a limited dependent variable mixture model performed better than an equivalent linear model .
3 ) The exploratory valuation study found that bolt-on items for vision , hearing and tiredness had a significant impact on values of the health states , but the direction and magnitude of differences depended on the severity of the health state .
The vision bolt-on item had a statistically significant impact on EQ-5D health state values and a full valuation model was estimated .
EQ-5D performs well in studies of cancer and skin conditions .
Mapping techniques provide a solution to predict EQ-5D values where EQ-5D has not been administered .
For conditions where EQ-5D was found to be inappropriate , including some vision disorders and for hearing , bolt-ons provide a promising solution .
BACKGROUND The National Institute for Health and Care Excellence recommends the use of generic preference-based measures ( GPBMs ) of health for its Health Technology Assessment s ( HTAs ) .
However , these data may not be available or appropriate for all health conditions .
OBJECTIVES To determine whether GPBMs are appropriate for some key conditions and to explore alternative methods of utility estimation when data from GPBMs are unavailable or inappropriate .
( 2 ) Potential modelling approaches to ' map ' EQ-5D values from condition-specific and clinical measures of health [ European Organisation for Research and Treatment of Cancer Quality -of-life Question naire Core 30 ( EORTC QLQ-C30 ) and Functional Assessment of Cancer Therapy - General Scale ( FACT-G ) ] are compared for predictive ability and goodness of fit using two separate data sets . (