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 . (
Health related quality of life in patients with multiple myeloma undergoing a double transplantation.
OBJECTIVES To investigate the subjective well-being of patients with newly diagnosed multiple myeloma who were treated in a t and em transplantation programme .  METHODS Fifty-one patients participated in the prospect i ve , longitudinal question naire study . The EORTC QLQ-C30 and the EuroQol-5D were administered 2 wk after completion of vincristine , adriamycin and dexamethason/vincristine , adriamycin and methyl prednison ( VAD/VAMP ) chemotherapy , both at hospital discharge after treatment with high-dose melphalan ( HDM ) and 1 month after this hospital discharge , at hospital admission , at the day of hospital discharge for peripheral stem cell transplantation ( PSCT ) and at 6 and 12 months following discharge after PSCT . RESULTS Overall , patients ' functioning improved during treatment and follow-up , with significant decreases shortly following PSCT . Shortly after HDM and PSCT , patients reported a considerable increase in levels of soreness in the mouth ( + 26/+36 points on a scale ranging form 0 to 100 ; P < 0.01 ) , change of taste ( + 23/+21 points ; P < 0.05/NS ) , nausea/vomiting ( + 26/+27 points ; P < 0.01/ < 0.05 ) , appetite loss ( + 40/+43 points ; P < 0.001 ) and diarrhoea ( + 25/+36 points ; P < 0.01 ) . However , none of these symptoms persisted during follow-up . CONCLUSION The intensive treatment programme was subjectively being well tolerated by the majority of patients . The duration of declined quality of life after administration of HDM seemed to be short . The duration of subjective recovery after PSCT remained uncertain , but in any case was present at the 6 month follow-up . Together with the rather good results in survival , the evaluation of quality of life invites further exploration of double transplantations in multiple myeloma
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 . (