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 . (
The bone-anchored hearing aid: quality-of-life assessment.
OBJECTIVES To assess the impact of a bone-anchored hearing aid ( BAHA )  on the  quality of life ( QOL ) of adults and to test the hypothesis that a BAHA improves QOL because otorrhea and /or skin irritations decrease . DESIGN Prospect i ve postal-based question naire study using vali date d health-related QOL instruments , combined with hearing-aid-related questions . PATIENTS AND METHODS The study included 56 consecutive adult patients with acquired conductive or mixed hearing loss who were scheduled for BAHA implantation at the University Medical Centre Nijmegen , Nijmegen , the Netherl and s. All 56 patients completed the 36-Item Short-Form  Health  Survey  ( SF-36 ) , the EuroQol-5D ( EQ-5D ) , and the Hearing H and icap and Disability Inventory ( HHDI ) ; 36 patients had been using an air-conduction hearing aid ( ACHA ) and 20 patients a conventional bone-conduction hearing aid ( CBHA ) .  Question naires were filled out before surgery and after 6 months of experience with the BAHA . RESULTS In the SF-36 group , there was significant improvement in the scores of the mental health domain ( P = .02 ) . When the SF-36 patients were classified according to previous hearing aid , there was no statistically significant change in the scores in any of the domains . In the EQ-5D group and in its ACHA and CBHA subgroups , there were no important differences in the results before and after the patients received their BAHAs . In the HHDI group , the h and icap and disability scales showed significant improvement ( P<.01 ) irrespective of the type of previously worn hearing aid . CONCLUSIONS Overall , generic health-related QOL was not influenced significantly by the use of a BAHA according to the SF-36 and the EQ-5D . The more disease-specific scales ( HHDI ) did show improved QOL with a BAHA
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 . (