Estimation of a Preference-Based Index from a Condition-Specific Measure: The King's Health Questionnaire Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences Study protocol for valuing EQ-5D-3L and EORTC-8D health states in a representative population sample in Sri Lanka Derivation of Utility Values from European Organization for Research and Treatment of Cancer Quality of Life-Core 30 Questionnaire Values in Lung Cancer Converting EORTC QLQ-C30 scores to utility scores in the brigatinib ALTA study Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments. Validity of the Assessment of Quality of Life (AQoL) utility instrument in patients with operable and inoperable lung cancer. Estimation of patient preference-based utility weights from the functional assessment of cancer therapy - general. Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D The potential for a generally applicable mapping model between QLQ-C30 and SF-6D in patients with different cancers: a comparison of regression-based methods The Quality of Life Questionnaire Core 30 (QLQ-C30) and Functional Assessment of Cancer-General (FACT-G) differ in responsiveness, relative efficiency, and therefore required sample size. Development of an EORTC-8D Utility Algorithm for Sri Lanka An empirical comparison of methods for analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening Preference-Based Measurement of Health-Related Quality of Life (HRQL) in Children with Chronic Musculoskeletal Disorders (MSKDs) Estimating a Preference-Based Index for an Eight-Dimensional Health State Classification System for Multiple Sclerosis. Patient preference for either the EORTC QLQ-C30 or the FACIT Quality Of Life (QOL) measures: a study performed in patients suffering from carcinoma of an unknown primary site (CUP). Mapping utility scores from a disease-specific quality-of-life measure in bariatric surgery patients.
Background . Generic preference-based measures of health may not adequately cover the impact of some conditions . There is therefore increasing interest in developing condition-specific preference-based measures . Objectives . The purpose of this study was to estimate a preference-based measure from a condition-specific measure of health for urinary incontinence , the 21-item King 's Health Question naire , for use in economic evaluation . Methods . The King 's Health Question naire ( KHQ ) was revised into a 5-dimensional health state classification amenable to valuation using items selected using psychometric evidence . Forty-nine states were valued using st and ard gamble by a representative sample of patients with urinary incontinence attending UK hospital outpatient clinics . Each respondent was asked to value 9 health states . Models have been estimated for predicting health state valuations for all 1024 states defined by the KHQ classification . The modeling had to cope with the clustering of data by respondent and its skewed distribution . Results . In total , 110 usable interviews were obtained from 169 patients approached to participate in the study . These responders generated 959 health state valuations . Mean health state values ranged from 0.77 to 0.98 . Models were estimated using mean health state values and r and om effects models of individual-level health state values . These models generated robust estimates of the ` main effects , ` and in general , the results support the ordinality of the KHQ health state classification . There were problems modeling interaction effects , and a number of alternatives were explored . Conclusion . The recommended model for estimating a preference-based measure from the condition-specific KHQ is presented Background Although cancer-specific Health-Related Quality of Life ( HRQOL ) are commonly included in r and omized clinical trials or other prospect i ve non-r and omized clinical studies , it is rare that preference-based instruments are used that allow the calculation of a utility weight suitable for estimating quality -adjusted life-years gained . Objective To develop a mapping algorithm to transform the EORTC QLQ-C30 question naire responses into EQ-5D derived utilities . Study design Retrospective data analysis of a multicentre , multicountry prospect i ve clinical trial in breast cancer patients . Methods Regression analysis of individual pairs of EQ-5D and QLQ-C30 scores . Results A model that explained 80 % of the variance was developed to estimate EQ-5D Utilities from QLQ-C30 scores at individual level . From this reliable group level means and deviations can be derived . Conclusions Mapping from QLQ-C30 scores to EQ-5D-derived utilities when only QLQ-C30 data are available has been shown to be possible with good accuracy . Validation of the proposed algorithm in other external clinical data sets should be encouraged Background Economic evaluations to inform decisions about allocation of health re sources are scarce in Low and Middle Income Countries , including in Sri Lanka . This is in part due to a lack of country-specific utility weights , which are necessary to derive appropriate Quality Adjusted Life Years . The EQ-5D-3L , a generic multi-attribute instrument ( MAUI ) , is most widely used to measure and value health states in high income countries ; nevertheless , the sensitivity of generic MAUIs has been criticised in some conditions such as cancer . This article describes a protocol to produce both a generic EQ-5D-3L and cancer specific EORTC-8D utility index in Sri Lanka . Method EQ-5D-3L and EORTC-8D health states will be valued using the Time Trade-Off technique , by a representative population sample ( n = 780 invited ) identified using stratified multi-stage cluster sampling with probability proportionate to size method . Households will be r and omly selected within 30 clusters across four districts ; one adult ( ≥18 years ) within each household will be selected using the Kish grid method . Data will be collected via face-to-face interview , with a Time Trade-Off board employed as a visual aid . Of the 243 EQ-5D-3L and 81,290 EORTC-8D health states , 196 and 84 respectively will be directly valued . In EQ-5D-3L , all health states that combine level 3 on mobility with either level 1 on usual activities or self-care were excluded . Each participant will first complete the EQ-5D-3L , rank and value 14 EQ-5D-3L states ( plus the worst health state and “ immediate death ” ) , and then rank and value seven EORTC-8D states ( plus “ immediate death ” ) . Participant demographic and health characteristics will be also collected . Regression models will be fitted to estimate utility indices for EQ-5D-3L and EORTC-8D health states for Sri Lanka . The dependent variable will be the utility value . Different specifications of independent variables will be derived from the ordinal EQ-5D-3L to test for the best-fitting model . Discussion In Sri Lanka , a LMIC health state valuation will have to be carried out using face to face interview instead of online methods . The proposed study will provide the first country-specific health state valuations for Sri Lanka , and one of the first valuations to be completed in a South Asian Country Introduction : Cancer clinical trials frequently incorporate quality of life ( QoL ) measures but rarely patient utility . Utility information is required for cost utility evaluations of novel cancer therapies . We assessed the feasibility of converting QoL data into utility scores using the European Organization for Research and Treatment of Cancer Quality of Life-Core  30 question naire ( EORTC QLQ-C30 ) and the EQ-5D in patients with non-small cell lung cancer ( NSCLC ) . Methods : Out patients with all different disease states of NSCLC attending a major Canadian cancer center completed the QLQ-C30 and EQ5D on a single visit . Results of the QLQ-C30 summary scores were mapped to predict EQ-5D utility scores using linear regression . Backward variable elimination using the Akaike Information Criterion was used to reduce the full model that included all QLQ-C30 summary scores to examine which QLQ-C30 dimensions best predict a patient 's utility score . To test the predictive power of the model , 10-fold cross-validation was used . Results : A total of 172 patients participated in the study . Median age of the sample was 66 years ( range , 32–85 years ) ; 46.5 % were men . The cross-validation estimate of mean utility score was 0.76 ( SD : 0.20 ) , which was the same as the actual mean utility score . Of the 15 QLQ-C30 dimensions , 4 functional dimensions ( physical , role , emotional , and social ) and the pain symptom dimension were predictive of patient utility scores . Conclusions : Our study demonstrates the feasibility of deriving utility scores from prospect i ve QoL data . Validation of the QLQ-C30 predictors found in this study could further the ability to estimate cost utility of therapies for economic evaluations Abstract Aims : Health utilities summarize a patient ’s overall health status . This study estimated utilities based on the European Organization for Research and Treatment of Cancer Quality of Life Question naire Core-30 ( QLQ-C30 ) , a widely used measure of health-related quality -of-life ( HRQoL ) in oncology , using published mapping algorithms . Material s and methods : Data were from the Anaplastic Lymphoma Kinase ( ALK ) in Lung Cancer Trial of brigatinib ( ALTA ; NCT02094573 ) , an open-label , international , phase 2 study . ALTA evaluated the efficacy and safety of two r and omized dosing regimens of brigatinib in patients with locally advanced or metastatic ALK + non-small cell lung cancer ( NSCLC ) that had progressed on prior therapy with crizotinib . QLQ-C30 scores were mapped to European Quality -of-Life-5 Dimensions ( EQ-5D ) utility scores using two published algorithms ( Khan et al. for EQ-5D-5L ; Longworth et al. for EQ-5D-3L ) . The impact of brigatinib treatment on health utilities over time was assessed . Results : The analysis included 208 subjects . Mean baseline utility scores for both algorithms ranged between 0.60 − 0.71 and increased to 0.78 by cycle 5 . Utility improvements were sustained during most of the treatment , before disease progression . Minor variations were observed between utility scores ; Khan et al. estimates were approximately 0.01 or 0.02 points lower than Longworth et al. estimates . Limitations : Algorithms considered were limited to those available in the published literature at the time of the study . This utility analysis was exploratory , and the ALTA trial did not include an internal control group ( i.e. st and ard of care ) and was not powered to detect differences in QoL/utility outcomes between treatment arms . Conclusions : Converting QLQ-C30 scores into utilities in trials using established mapping algorithms can improve evaluation of medicines from the patient perspective . Both algorithms suggested that brigatinib  improved health utility in crizotinib-refractory ALK + NSCLC patients , and improvements were maintained during most of the treatment . Clinical trials.gov identifier : 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 There have been few longitudinal studies of quality of life in patients with all stages of lung cancer , particularly those that have included measures of utility . The purpose of this study was to examine the psychometric properties of the Assessment of Quality of Life instrument ( AQoL )  in patients with lung cancer . The AQoL is a health-related quality of life question naire and provides a descriptive system for a multi-attribute utility instrument ( MAU ) , so that scores can be used in cost-utility evaluations . In the present study the reliability ( internal consistency ) of the AQoL was examined and the concurrent validity was assessed using the Medical Outcomes 36-item Short Form Health Survey ( SF-36 ) as the comparator instrument . The sensitivity to different health states of the AQoL and the responsiveness to change over time was also examined . A prospect i ve , non-experimental cohort study was undertaken . Ninety-two participants with all stages of lung cancer were recruited from a tertiary multi-disciplinary lung cancer clinic . Ninety participants had non-small cell lung cancer ( NSCLC ) and two had limited stage small cell lung cancer . The  AQOL and SF-36 surveys  were administered concurrently at baseline . In patients with NSCLC the surveys were then repeated 3 and 6 months later . Correlations between the baseline AQoL summary scales and SF-36 summary scales support the divergent and convergent validity of the AQoL.  Reliability was also found to be sufficient ( Cronbach 's Alpha=0.76 ) . In addition , in patients with inoperable NSCLC , baseline AQoL scores were found to be predictive of survival at 6 months in Cox proportional hazards multivariate analysis . However , the physical components summary score of the SF-36 was more sensitive to differences in health states between patients with different stages of NSCLC at 6 months of follow-up and more responsive to change over time in both operable and inoperable patients with NSCLC than the AQoL. The findings support the construct validity and reliability of the  AQoL  in this population . However , there remains some uncertainty about whether the AQoL has sufficient sensitivity to different health states in this population . Further studies using other MAU instruments may determine whether alternative instruments are more sensitive to different health states in individuals with lung cancer OBJECTIVE The goal of this study was to estimate an algorithm to convert responses to the Functional Assessment of Cancer Therapy - General ( FACT-G ) to time trade-off ( TTO ) utilities based on utilities for current health elicited from cancer patients . METHODS Data for 1433 cancer patients were r and omly separated into construction and validation sample s. Four FACT-G questions were selected for inclusion based on correlation with Eastern Clinical Oncology Group - Performance Status ( ECOG-PS ) scores and TTO utilities . Item response theory was used to collapse response categories . Ordinary least squares regression with the constant constrained to one was used to estimate the algorithm . RESULTS The algorithm estimated mean utility for the full validation sample within three points of observed mean utility ( 0.805 vs. 0.832 , P < 0.01 ) . Mean utilities were well predicted ( mean absolute difference < 0.03 , P > 0.05 ) for most subgroups defined by ECOG-PS and Short Form-36 physical functioning scores , and responses to the FACT-G overall quality of life item . Nevertheless , the algorithm systematic ally overpredicted utilities for poorer health states . CONCLUSIONS A FACT-G-based algorithm of cancer patient utilities was developed that estimates group mean utility scores with accuracy comparable to other indirect preference-based measures of health-related quality of life . Patient-based preferences for health outcomes of cancer treatment may be useful in multiple situations , such as managing re sources within cancer centers and in underst and ing health states preferences among cancer experienced patients before and after treatment Objective To help facilitate economic evaluations of oncology treatments , we mapped responses on cancer-specific instrument to generic preference-based measures . Methods Cancer patients ( n = 367 ) completed one cancer-specific instrument , the FACT-G , and two preference-based measures , the  EQ-5D and SF-6D . Responses were r and omly divided to form development ( n = 184 ) and cross-validation ( n = 183 ) sample s. Relationships between the instruments were estimated using ordinary least squares ( OLS ) , generalized linear models ( GLM ) , and censored least absolute deviations ( CLAD ) regression approaches . The performance of each model was assessed in terms of how well the responses to the cancer-specific instrument predicted EQ-5D and SF-6D utilities using mean absolute error ( MAE ) and root mean squared error ( RMSE ) . Results Physical , functional , and emotional well-being domain scores of the FACT-G best explained the EQ-5D and SF-6D . In terms of accuracy of prediction as measured in RMSE , the CLAD model performed best for the EQ-5D ( RMSE = 0.095 ) whereas the GLM model performed best for the SF-6D ( RMSE = 0.061 ) . The GLM predicted SF-6D scores matched the observed values more closely than the CLAD and OLS . Conclusion Our results demonstrate that the estimation of both EQ-5D and SF-6D utility indices using the FACT-G responses can be achieved . The CLAD model for the EQ-5D and the GLM model for the SF-6D are recommended . Thus , it is possible to estimate quality -adjusted life years for economic evaluation from studies where only cancer-specific instrument have been administered Purpose To establish and compare generalized or “ global ” mapping relationships between QLQ-C30 and SF-6D , applicable across different cancer types . Methods Patients ( N = 671 ) with breast , myeloma , colorectal , lymphoma , bone marrow , prostate , lung and gastroenteric cancer were r and omly split into estimation ( 75 % ) and validation ( 25 % ) data sets . SF-6D was estimated from QLQ-C30 scores via ordinary least squares , generalized linear models and median ( least-absolute deviations ) regression approaches , and with Bayesian additive regression kernels . Predictive ability was assessed with root mean square error , mean absolute error and proportions of predictions with absolute errors > 0.05 and > 0.1 , whereas explanatory power with adjusted R2 or equivalent fit measures . Two external sample s ( breast and colorectal cancer ) were used to further test the models . Results The QLQ-C30 's global health item , the physical , emotional and social functioning scales , and the fatigue , pain and diarrhea symptom scales were significant predictors ( p < 0.05 or better ) in all models . Negligible deviations in models ’ performance were observed . All models overpredicted utilities for patients in worst health and underpredicted them for those in better health ( p < 0.01 or better ) . Regarding external validation , performance was better in the colorectal cancer than in the breast cancer sample . Conclusions This study has provided evidence to support the use of “ global ” mapping models to predict SF-6D utilities from QLQ-C30 in patients with different cancers . Testing with diverse patient sample s is required to confirm the generalizability ( or not ) of mapping models across cancer conditions OBJECTIVES  Quality of Life Question naire Core 30 ( QLQ-C30 ) and Functional Assessment of Cancer Therapy-General ( FACT-G ) are widely used cancer-specific health-related  quality of life  ( HRQOL  ) question naires . We aim ed to compare their responsiveness with clinical ly important effects and statistical efficiency to detect such effects . STUDY DESIGN AND SETTING Secondary analysis of QLQ-C30 and FACT-G data from a r and omized controlled trial of Medical Qigong ( n = 162 heterogeneous cancer patients ) .  Difference in responsiveness ( DR ) and relative efficiency ( RE ) were calculated for five domains . RESULTS FACT-G total score was more efficient than QLQ-C30 global scale for detecting change within the intervention arm [ RE = 0.31 ( 0.083 , 0.69 ) ] and comparing change between trial arms [ RE = 0.17 ( 0.009 , 0.58 ) ] . In the social domain , the QLQ-C30 scale was more responsive [ DR = 0.28 ( 0.024 , 0.54 ) ] and more efficient within arm only [ RE = 5.25 ( 1.21 , 232.26 ) ] . In the physical , functional/role , and emotional domains , neither question naire was more responsive or efficient . CONCLUSION FACT-G would require about one-third the sample of QLQ-C30 to detect a given change in overall HRQOL , whereas in the social domain , it would require five times the sample size . FACT-G won advantage in overall HRQOL by reduced " noise " ( smaller st and ard deviation achieved by summing across 27 items ) , whereas QLQ-C30 won advantage in the social domain via a larger " signal " ( achieved through well-targeted item content ) Background . Currently there are no reported cancer-specific health state valuations in low- and middle-income countries using a vali date d preference-based measure . The EORTC-8D , a cancer-specific preference-based measure , has 81,920 health states and is useful for economic evaluations in cancer care . The aim of this study was to develop a utility algorithm to value EORTC-8D health states using preferences derived from a representative population sample in Sri Lanka . Methods . The time-tradeoff method was used to elicit preferences from a general population sample of 780 in Sri Lanka . A block design of 85 health states , with a time horizon of 10 years , was used for the direct valuation . Data were analyzed using generalized least squares with r and om effects . All respondents with at least one logical inconsistency were excluded from the analysis . Results . After logical inconsistencies were excluded , 4520 observations were available from 717 respondents for the analysis . The preferred model specified main effects with an interaction term for any level 4 or worse descriptor within a health state . Worsening of physical functioning had a substantially greater utility decrement than any other dimension in this population . Limitations are that the data collection could not include the whole country and that females formed a large part of the sample . Conclusions . Preference weights for EORTC-8D health states for Sri Lanka have been derived : These will be very useful in economic evaluations of cancer-related interventions in a range of low- and middle-income countries Background A discrete choice experiment ( DCE ) is a preference survey which asks participants to make a choice among product portfolios comparing the key product characteristics by performing several choice tasks . Analyzing DCE data needs to account for within-participant correlation because choices from the same participant are likely to be similar . In this study , we empirically compared some commonly-used statistical methods for analyzing DCE data while accounting for within-participant correlation based on a survey of patient preference for colorectal cancer ( CRC ) screening tests conducted in Hamilton , Ontario , Canada in 2002 . Methods A two-stage DCE design was used to investigate the impact of six attributes on participants ' preferences for CRC screening test  and willingness to undertake the test . We compared six models for clustered binary outcomes ( logistic and probit regressions using cluster-robust st and ard error ( SE ) , r and om-effects and generalized estimating equation approaches ) and three models for clustered nominal outcomes ( multinomial logistic and probit regressions with cluster-robust SE and r and om-effects multinomial logistic model ) . We also fitted a bivariate probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes . The rank of relative importance between attributes and the estimates of βcoefficient within attributes were used to assess the model robustness . Results In total 468 participants with each completing 10 choices were analyzed . Similar results were reported for the rank of relative importance and βcoefficients across models for stage-one data on evaluating participants ' preferences for the test . The six attributes ranked from high to low as follows : cost , specificity , process , sensitivity , preparation and pain . However , the results differed across models for stage-two data on evaluating participants ' willingness to undertake the tests . Little within-patient correlation ( ICC ≈ 0 ) was found in stage-one data , but substantial within-patient correlation existed ( ICC = 0.659 ) in stage-two data . Conclusions When small clustering effect presented in DCE data , results remained robust across statistical models . However , results varied when larger clustering effect presented . Therefore , it is important to assess the robustness of the estimates via sensitivity analysis using different models for analyzing clustered data from DCE studies Background . Health-related quality of life can be measured by patients ' health preferences ( utilities or values ) . No method for measuring health state preferences has been st and ardized for children with arthritis or other musculoskeletal disorders ( MSKDs ) . Such a method is needed for economic evaluations of current and new pediatric treatments . Objectives . 1 ) To assess the feasibility of utility measurements in children with MSKDs , 2 ) to test the validity of the Health Utility Index ( HUI ) for these children , 3 ) to assess whether rating scale values can be mathematically converted into meaningful st and ard gamble ( SG ) utilities , and 4 ) to study whether parents can act as proxies for their children with respect to health state preferences . Methods . Eighty parents of children with MSKDs were consecutively sample d. Their children , if 8 years of age or older ( n = 55 ) , were studied concurrently . Utilities of current health states were obtained by using the SG and the HUI in r and om order . In addition , health state preferences were assessed using categorical and analog rating scales . Traditional nonutility measures of health status ( the Childhood Health Assessment Question naire [ CHAQ ] and the Activities Scale for Kids [ ASK ] ) were also completed . Intraclass correlation coefficients ( ICCs ) were calculated to assess concordance between the different utility measures and also between the ratings of the parents and their children . Results . Children 8 years of age or older were able to express the strength of their health state preferences using the HUI and rating scales . Children older than 10 years of age were able to use the SG method . The health state utilities of the parents were higher than those of their children . The utilities varied widely depending on the elicitation method . The expected high agreement between the SG and the HUI was not found ( ICC = 0.028 for parents , ICC = 0.016 for patients ) . Unlike the SG , the global utilities derived from the HUI agreed better with preferences derived from rating scales ( ICC = 0.23 - 0.25 ) and correlated with traditional health status measures ( with CHAQ , r = -0.56 ; with ASK , r = 0.46 ) both for parents and children . It was not possible to mathematically convert rating scale preferences into SG utilities . The SG utilities were unrelated to results from the rating scales , the CHAQ , and the ASK . Especially for parents , the SG utilities were very high , even when ratings of the other measures indicated poor health . Conclusions . Although it is possible to measure health utilities for children with MSKDs , the results are highly method dependent . The properties of the HUI in this population are more like those of the traditional health status measures rather than those of the SG . Preferences derived from rating scales , although easily performed , can not readily be converted into SG utilities . Parents ' ratings for their children are impaired by risk aversion BACKGROUND Condition-specific measures are frequently used to assess the health-related quality of life of people with multiple sclerosis ( MS ) . Such measures are unsuitable for use in economic evaluations that require estimates of cost per quality -adjusted life-year because they are not based on preferences . OBJECTIVES To report the estimation of a preference-based single index for an eight-dimensional instrument for MS , the Multiple Sclerosis Impact Scale - Eight Dimensions ( MSIS-8D ) , derived from an MS-specific measure of health-related quality of life , the 29-item Multiple Sclerosis Impact Scale ( MSIS-29 ) . METHODS We elicited preferences for a sample of MSIS-8D states ( n = 169 ) from a sample ( n = 1702 ) of the UK general population . Preferences were elicited using the time trade-off technique via an Internet-based survey . We fitted regression models to these data to estimate values for all health states described by the MSIS-8D . Estimated values were assessed against MSIS-29 scores and values derived from generic preference-based measures in a large , representative sample of people with MS . RESULTS Participants reported that the time trade-off questions were easy to underst and . Observed health state values ranged from 0.08 to 0.89 . The best-performing model was a main effects , r and om effects model ( mean absolute error = 0.04 ) . Validation analyses support the performance of the MSIS-8D index : it correlated more strongly than did generic measures with MSIS-29 scores , and it discriminated effectively between subgroups of people with MS . CONCLUSIONS The MSIS-8D enables health state values to be estimated from the MSIS-29 , adding to the methods available to assess health outcomes and to estimate quality -adjusted life-years for MS for use in health technology assessment and decision-making context The objective of this study was to examine and compare two core measures of Quality Of Life ( QOL ) used in cancer clinical trials : the European Organisation for Research and Treatment of Cancer QOL  Core Question naire 30 ( EORTC QLQ-30 ) and the Functional Assessment of Chronic Illness Therapy ( FACIT ) , in order to identify which one patients have the strongest preference for using . 68 patients suffering from Carcinomas of an Unknown Primary site ( CUP ) were recruited in a multicentric study ; all of them completed both question naires , administered in a r and omised manner . The criteria were the percentage of preferences , and four indicators of acceptability . The results indicated that an equal proportion of patients preferred the QLQ-C30 ( 19 % ) and FACIT ( 19 % ) . 54 % of patients felt both question naires were acceptable . All the indicators of acceptability favoured the QLQ-C30 . Analysis of open-ended questions shed light on the difficulties encountered by the patients . As no significant preference was observed for one of the question naires , the QLQ-C30 was chosen on the basis of its significantly better acceptability criteria OBJECTIVES To develop algorithms for a conversion of disease-specific quality -of-life into health state values for morbidly obese patients before or after bariatric surgery . METHODS A total of 893 patients were enrolled in a prospect i ve cross-sectional multicenter study . In addition to demographic and clinical data , health-related quality -of-life ( HRQoL ) data were collected using the disease-specific Moorehead-Ardelt II question naire ( MA-II ) and two generic question naires , the EuroQoL-5D ( EQ-5D ) and the Short Form-6D ( SF-6D ) . Multiple regression models were constructed to predict EQ-5D- and SF-6D-based utility values from MA-II scores and additional demographic variables . RESULTS The mean body mass index was 39.4 , and 591 patients ( 66 % ) had already undergone surgery . The average EQ-5D and SF-6D scores were 0.830 and 0.699 . The MA-IIwas correlated to both utility measures ( Spearman 's r = 0.677 and 0.741 ) . Goodness-of-fit was highest ( R(2 ) = 0.55 in the validation sample ) for the following item-based transformation algorithm : utility ( MA-II-based ) = 0.4293 + ( 0.0336 x MA1 ) + ( 0.0071 x MA2 ) + ( 0.0053 x MA3 ) + ( 0.0107 x MA4 ) + ( 0.0001 x MA5 ) . This EQ-5D-based mapping algorithm outperformed a similar SF-6D-based algorithm in terms of mean absolute percentage error ( P = 0.045 ) . CONCLUSIONS Because the mapping algorithm estimated utilities with only minor errors , it appears to be a valid method for calculating health state values in cost-utility analyses . The algorithm will help to define the role of bariatric surgery in morbid obesity
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 . (