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 . (
Quality of life in patients with newly diagnosed chronic phase chronic myeloid leukemia on imatinib versus interferon alfa plus low-dose cytarabine: results from the IRIS Study.
PURPOSE Quality of life ( QOL ) outcomes in patients with chronic myeloid leukemia ( CML ) were evaluated in an international phase III study . PATIENTS AND METHODS Newly diagnosed patients with chronic phase CML were r and omly assigned to imatinib or interferon alfa plus subcutaneous low-dose cytarabine ( IFN+LDAC ) . Cross-over to the other treatment was permitted because of intolerance or lack of efficacy . Patients completed cancer-specific QOL ( Functional Assessment of Cancer Therapy-Biologic Response Modifiers ) and utility ( Euro QoL-5D ) question naires at baseline and during treatment ( n = 1,049 ) . The primary QOL end point was the Trial Outcome Index ( TOI ; a measure of physical function and well-being ) . Secondary end points included social and family well-being ( SFWB ) , emotional well-being ( EWB ) , and the utility score . Primary analyses were intention to treat with secondary analyses accounting for cross-over . RESULTS Patients receiving IFN+LDAC experienced a large decline in the TOI , whereas those receiving imatinib maintained their baseline level . Treatment differences at each visit were significant ( P < .001 ) and clinical ly relevant in favor of imatinib .  Mean SFWB , EWB , and utility scores were also significantly better for those patients taking imatinib . Patients who crossed over to imatinib experienced a large increase in TOI ; significant ( P < .001 ) differences were observed between patients who did and did not cross over in favor of imatinib . CONCLUSION Imatinib offers clear QOL advantages compared with IFN+LDAC as first-line treatment of chronic phase CML . In addition , patients who cross over to imatinib from IFN+LDAC experience a significant improvement in QOL compared with patients who continue to take IFN+LDAC
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 . (