Mapping scores from the Strengths and Difficulties Questionnaire (SDQ) to preference-based utility values Mapping the Paediatric Quality of Life Inventory (PedsQL™) Generic Core Scales onto the Child Health Utility Index–9 Dimension (CHU-9D) Score for Economic Evaluation in Children Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression Mapping utility scores from a disease-specific quality-of-life measure in bariatric surgery patients. Mapping from disease-specific measures to health-state utility values in individuals with migraine. Paving the way for the use of the SDQ in economic evaluations of school-based population health interventions: an empirical analysis of the external validity of SDQ mapping algorithms to the CHU9D in an educational setting Assessment of patient preferences among men with prostate cancer. Estimating a preference-based index for a menopause specific health quality of life questionnaire Mapping Between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and Five Multi-Attribute Utility Instruments (MAUIs) Mapping SF-36 onto the EQ-5D index: how reliable is the relationship? The Assessment of Quality of Life (AQoL) instrument: a psychometric measure of Health-Related Quality of Life Comparison of methods to estimate health state utilities for ovarian cancer using quality of life data: a Gynecologic Oncology Group study. Modeling a preference-based index for two condition-specific measures (asthma and overactive bladder) using a nonparametric Bayesian method. Developing a utility index for the Aberrant Behavior Checklist (ABC-C) for fragile X syndrome Assessing outcomes for cost-utility analysis in mental health interventions: mapping mental health specific outcome measure GHQ-12 onto EQ-5D-3L Health-Related Quality of Life and Utility in Patients with Advanced Non–Small-Cell Lung Cancer: A Prospective Cross-Sectional Patient Survey in a Real-World Setting Economic evaluation in short bowel syndrome (SBS): an algorithm to estimate utility scores for a patient-reported SBS-specific quality of life scale (SBS-QoL™) Mapping the FACT-G cancer-specific quality of life instrument to the EQ-5D and SF-6D Mapping between the Roland Morris Questionnaire and generic preference-based measures. Valuing the Child Health Utility 9D: Using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm. Bayesian predictors of very poor health related quality of life and mortality in patients with COPD Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D
Abstract Purpose Quality of life mapping methods such as “ Transfer to Utility ” can be used to translate scores on disease-specific measures to utility values , when traditional utility measurement methods ( e.g. st and ard gamble , time trade-off , preference-based multi-attribute instruments ) have not been used . The aim of this study was to generate preliminary ordinary least squares ( OLS ) regression-based algorithms to transform scores from the Strengths and Difficulties Question naires ( SDQ ) , a widely used measure of mental health in children and adolescents , to utility values obtained using the preference-based Child Health Utility ( CHU9D ) instrument . Methods Two hundred caregivers of children receiving community mental health services completed the  SDQ and CHU9D  during a telephone interview . Two OLS regressions were run with the CHU9D utility value as the dependent variable and SDQ subscales as predictors . Result ing algorithms were vali date d by comparing predicted and observed group mean utility values in r and omly selected sub sample s. Results Preliminary validation was obtained for two algorithms , utilising five and three subscales of the SDQ , respectively . Root mean square error values ( .124 ) for both models suggested poor fit at an individual level , but both algorithms performed well in predicting mean group observed utility values . Conclusion This research generated algorithms for translating SDQ scores to utility values and providing research ers with an additional tool for conducting health economic evaluations with child and adolescent mental health data Background The Paediatric Quality of Life Inventory ( PedsQL ™ ) question naire is a widely used , generic instrument design ed for measuring health-related quality of life ( HRQoL ) ; however , it is not preference-based and therefore not suitable for cost – utility analysis . The Child Health Utility Index–9 Dimension ( CHU-9D ) , however , is a preference-based instrument that has been primarily developed to support cost – utility analysis . Objective This paper presents a method for estimating CHU-9D index scores from responses to the PedsQL ™ using data from a r and omised controlled trial of prednisolone therapy for treatment of childhood corticosteroid-sensitive nephrotic syndrome . Methods HRQoL data were collected from children at r and omisation , week 16 , and months 12 , 18 , 24 , 36 and 48 . Observations on children aged 5 years and older were pooled across all data collection timepoints and were then r and omised into an estimation ( n = 279 ) and validation ( n = 284 ) sample . A number of models were developed using the estimation data before internal validation . The best model was chosen using multi-stage selection criteria . Results Most of the models developed accurately predicted the CHU-9D mean index score . The best performing model was a generalised linear model ( mean absolute error = 0.0408 ; mean square error = 0.0035 ) . The proportion of index scores deviating from the observed scores by < 0.03 was 53 % . Conclusions The mapping algorithm provides an empirical tool for estimating CHU-9D index scores and for conducting cost – utility analyses within clinical studies that have only collected PedsQL ™ data . It is valid for children aged 5 years or older . Caution should be exercised when using this with children younger than 5 years , older adolescents ( > 13 years ) or patient groups with particularly poor quality of life . IS RCT N Registry 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 Purpose The Dermatology  Life Quality  Index ( DLQI ) and the European Quality of Life-5 Dimension ( EQ-5D ) are separate measures that may be used to gather health-related quality of life ( HRQoL ) information from patients . The EQ-5D is a generic measure from which health utility estimates can be derived , whereas the DLQI is a specialty-specific measure to assess HRQoL. To reduce the burden of multiple measures being administered and to enable a more disease-specific calculation of health utility estimates , we explored an established mathematical technique known as ordinal logistic regression ( OLR ) to develop an appropriate model to map DLQI data to EQ-5D-based health utility estimates . Methods Retrospective data from 4010 patients were r and omly divided five times into two groups for the derivation and testing of the mapping model . Split-half cross-validation was utilized result ing in a total of ten ordinal logistic regression models for each of the five EQ-5D dimensions against age , sex , and all ten items of the DLQI . Using Monte Carlo simulation , predicted health utility estimates were derived and compared against those observed . This method was repeated for both OLR and a previously tested mapping methodology based on linear regression . Results The model was shown to be highly predictive and its repeated fitting demonstrated a stable model using OLR as well as linear regression . The mean differences between OLR-predicted health utility estimates and observed health utility estimates ranged from 0.0024 to 0.0239 across the ten modeling exercises , with an average overall difference of 0.0120 ( a 1.6 % underestimate , not of clinical importance ) . Conclusions This modeling framework developed in this study will enable research ers to calculate EQ-5D health utility estimates from a specialty-specific study population , reducing patient and economic burden 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 OBJECTIVE The objective of this study was to develop empirical algorithms that estimate health-state utility values from disease-specific quality -of-life scores in individuals with migraine . METHODS Data from a cross-sectional , multicountry study were used . Individuals with episodic and chronic migraine were r and omly assigned to training or validation sample s. Spearman 's correlation coefficients between paired EuroQol five-dimensional ( EQ-5D ) question naire utility values and both Headache Impact Test ( HIT-6 ) scores and Migraine-Specific Quality -of-Life Question naire version 2.1 ( MSQ ) domain scores ( role restrictive , role preventive , and emotional function ) were examined . Regression models were constructed to estimate EQ-5D question naire utility values from the HIT-6 score or the MSQ domain scores . Preferred algorithms were confirmed in the validation sample s. RESULTS In episodic migraine , the preferred HIT-6 and MSQ algorithms explained 22 % and 25 % of the variance ( R(2 ) ) in the training sample s , respectively , and had similar prediction errors ( root mean square errors of 0.30 ) . In chronic migraine , the preferred HIT-6 and MSQ algorithms explained 36 % and 45 % of the variance in the training sample s , respectively , and had similar prediction errors ( root mean square errors 0.31 and 0.29 ) . In episodic and chronic migraine , no statistically significant differences were observed between the mean observed and the mean estimated EQ-5D question naire utility values for the preferred HIT-6 and MSQ algorithms in the validation sample s. CONCLUSIONS The relationship between the EQ-5D question naire and the HIT-6 or the MSQ is adequate to use regression equations to estimate EQ-5D question naire utility values . The preferred HIT-6 and MSQ algorithms will be useful in estimating health-state utilities in migraine trials in which no preference-based measure is present Abstract Purpose The Strengths and Difficulties Question naire ( SDQ ) is a behavioural screening tool for children . The SDQ is increasingly used as the primary outcome measure in population health interventions involving children , but it is not preference based ; therefore , its role in allocative economic evaluation is limited . The Child Health Utility 9D ( CHU9D ) is a generic preference-based health-related quality of-life measure . This study investigates the applicability of the SDQ outcome measure for use in economic evaluations and examines its relationship with the CHU9D by testing previously published mapping algorithms . The aim of the paper is to explore the feasibility of using the SDQ within economic evaluations of school-based population health interventions . Methods Data were available from children participating in a cluster r and omised controlled trial of the school-based roots of empathy programme in Northern Irel and . Utility was calculated using the original and alternative CHU9D tariffs along with two SDQ mapping algorithms . t tests were performed for pairwise differences in utility values from the preference-based tariffs and mapping algorithms . Results Mean ( st and ard deviation ) SDQ total difficulties and prosocial scores were 12 ( 3.2 ) and 8.3 ( 2.1 ) . Utility values obtained from the original tariff , alternative tariff , and mapping algorithms using five and three SDQ subscales were 0.84 ( 0.11 ) , 0.80 ( 0.13 ) , 0.84 ( 0.05 ) , and 0.83 ( 0.04 ) , respectively . Each method for calculating utility produced statistically significantly different values except the original tariff and five SDQ subscale algorithm . Conclusion Initial evidence suggests the SDQ and CHU9D are related in some of their measurement properties . The mapping algorithm using five SDQ subscales was found to be optimal in predicting mean child health utility . Future research valuing changes in the SDQ scores would contribute to this research PURPOSE We developed a self-administered paper based instrument to assess patient preferences quantified as utilities for common outcomes associated with the management of prostate cancer . MATERIAL S AND METHODS A total of 50 patients was invited to test a self-administered paper based instrument design ed to assess preferences for health outcomes associated with the management of localized prostate cancer . The 50 patients were selected from a group of 625 r and omly identified men with prostate cancer who responded to a survey instrument design ed to assess health related quality of life . The 50 patients selected for this pilot project were chosen because of the wide range of responses to the  quality of life survey . Patient utilities were assessed for the 5 health states of overall quality of life , problems related to prostate cancer , and problems related to urinary , bowel and sexual dysfunction . RESULTS Patients were able to complete the assigned tasks . The self-administered instrument had high test-retest reliability . In addition results obtained from this instrument showed a correlation with results obtained from assessment s using other instruments , including an analog scale , a computer based system known as U-Titer , a quality of life survey and the Health Utility Index:3 . CONCLUSIONS A self-administered paper based instrument can be used to assess patient utilities for health states associated with prostate cancer management . Results from the instrument tested appear to be reliable and valid , and are comparable to those obtained from other assessment techniques . A self-administered paper based instrument has distinct advantages when conducting large survey studies because it can be incorporated at relatively low cost Background The aim of the study was to develop a menopause-specific , preference-based health-related quality -of-life ( HRQoL ) index reflecting both menopausal symptoms and potential side-effects of Hormone Replacement Therapy ( HRT ) . Methods The study had three phases : the development of a health state classification , a prospect i ve valuation survey and the estimation of a model to interpolate HRQoL indices for all remaining health states as defined by the classification . A menopausal health state classification was developed with seven dimensions : hot flushes , aching joints/muscles , anxious/frightened feelings , breast tenderness , bleeding , vaginal dryness and undesirable and rogenic signs . Each dimension contains between three and five levels and defines a total of 6,075 health states . A sample of 96 health states was selected for the valuation survey . These states were valued by a sample of 229 women aged 45 to 60 , r and omly selected from 6 general practice lists in Sheffield , UK . Respondents were asked to complete a time trade-off ( TTO ) task for nine health states , result ing in an average of 16.5 values for each health state . Results Mean health states valued range from 0.48 to 0.98 ( where 1.0 is full health and zero is for states regarded as equivalent to death ) . Symptoms , as described by the classification system , can be rank-ordered in terms of their impact ( from high to low ) on menopausal HRQoL as follows : aching joints and muscles , bleeding , breast tenderness , anxious or frightened feelings , vaginal dryness , and rogenic signs . Hot flushes did not significantly contribute to model fit . The preferred model produced a mean absolute error of 0.053 , but suffered from bias at both ends of the scale . Conclusion This article presents an attempt to directly value a condition specific health state classification . The overall fit was disappointing , but the results demonstrate that menopausal symptoms are perceived by patients to have a significant impact on utility . The overall effect is modest compared to the more generic health state descriptions such as the EQ-5D . The result ant algorithm generates a preference-based index that can be used economic evaluation and that reflects the impact of this condition Purpose Economic evaluation of health services commonly requires information regarding health-state utilities . Sometimes this information is not available but non-utility measures of quality of life may have been collected from which the required utilities can be estimated . This paper examines the possibility of mapping a non-utility-based outcome , the Sydney Asthma Quality of Life Question naire ( AQLQ-S ) , onto five multi-attribute utility instruments :  Assessment of Quality of Life 8 Dimensions ( AQoL-8D ) ,  EuroQoL 5 Dimensions 5-Level ( EQ-5D-5L ) , Health Utilities Index Mark 3 ( HUI3 ) , 15 Dimensions ( 15D ) , and the Short-Form 6 Dimensions ( SF-6D ) . Methods Data for 856 individuals with asthma were obtained from a large Multi-Instrument Comparison ( MIC ) survey . Four statistical techniques were employed to estimate utilities from the AQLQ-S. The predictive accuracy of 180 regression models was assessed using six criteria : mean absolute error ( MAE ) , root mean squared error ( RMSE ) , correlation , distribution of predicted utilities , distribution of residuals , and proportion of predictions with absolute errors < 0.0.5 . Validation of initial ‘ primary ’ models was carried out on a r and om sample of the MIC data . Results Best results were obtained with non-linear models that included a quadratic term for the AQLQ-S score along with demographic variables . The four statistical techniques predicted models that performed differently when assessed by the six criteria ; however , the best results , for both the estimation and validation sample s , were obtained using a generalised linear model ( GLM estimator ) . Conclusions It is possible to predict valid utilities from the AQLQ-S using regression methods . We recommend GLM models for this exercise Background Mapping from health status measures onto generic preference-based measures is becoming a common solution when health state utility values are not directly available for economic evaluation . However the accuracy and reliability of the models employed is largely untested , and there is little evidence of their suitability in patient data sets . This paper examines whether mapping approaches are reliable and accurate in terms of their predictions for a large and varied UK patient data set . Methods SF-36 dimension scores are mapped onto the EQ-5D index using a number of different model specifications . The predicted EQ-5D scores for subsets of the sample are compared across inpatient and outpatient setting s and medical conditions . This paper compares the results to those obtained from existing mapping functions . Results The model including SF-36 dimensions , squared and interaction terms estimated using r and om effects GLS has the most accurate predictions of all models estimated here and existing mapping functions as indicated by MAE ( 0.127 ) and MSE ( 0.030 ) . Mean absolute error in predictions by EQ-5D utility range increases with severity for our models ( 0.085 to 0.34 ) and for existing mapping functions ( 0.123 to 0.272 ) . Conclusion Our results suggest that models mapping the SF-36 onto the EQ-5D have similar predictions across inpatient and outpatient setting and medical conditions . However , the models overpredict for more severe EQ-5D states ; this problem is also present in the existing mapping functions This paper describes constructing the Assessment of Quality of Life ( AQoL ) instrument ; design ed to measure health-related quality of life ( HRQoL )  , and to be the descriptive system for a multi-attribute utility instrument . Unlike previous utility instruments ' descriptive systems , the AQoL 's has been developed using state-of-the-art psychometric procedures . The result is a descriptive system which emphasizes five different facets of HRQoL and which can cl aim to have construct validity . Based on the WHO 's definition of health a model of HRQoL was developed . Items were written by focus groups of doctors and the research ers . These were administered to a construction sample , comprising hospital patients , and community members chosen at r and om . Final construction was through an iterative process of factor and reliability analyses . The AQoL measures 5 dimensions : illness , independent living , social relationships , physical senses and psychological wellbeing . Each has three items . Exploratory factor analysis showed the dimensions were orthogonal , and each was unidimensional . Internal consistency was α = 0.81 . Structural equation modeling explored its internal structure ; the comparative fit index was 0.90 . These preliminary results indicate the AQoL has the prerequisite qualities for a psychometric HRQoL instrument for evaluation ; replication with a larger sample is needed to verify these findings . Scaling it for economic evaluation using utilities is being undertaken . Respondents have indicated the AQoL is easy to underst and and is quickly completed . Its initial properties suggest it may be widely applicable BACKGROUND Cost-effectiveness/cost-utility analyses are increasingly needed to inform decisions about care . Algorithms have been developed using the Functional Assessment of Cancer Therapy ( FACT ) quality of life instrument to estimate utility weights for cost analyses . This study was design ed to compare these algorithms in the setting of ovarian cancer . METHODS GOG-0152 was a 550-patient r and omized phase III trial of interval cytoreduction , and GOG-0172 was a 415-patient r and omized phase III trial comparing intravenous versus intraperitoneal therapy among women with advanced ovarian cancer .  QOL data were collected via the FACT at four time points in each study . Two published mapping algorithms ( Cheung and Dobrez ) and a linear transformation method were applied to these data . The agreement between measures was assessed by the concordance correlation coefficient ( r(CCC ) ) , and paired t-tests were used to compare means . RESULTS While agreement between the estimation algorithms was good ( ranged from 0.72 to 0.81 ) , there were statistically significant ( p<0.001 ) and clinical ly meaningful differences between the scores : mean scores were higher with Dobrez than with Cheung or the linear transformation method . Scores were also statistically significantly different ( p<0.001 ) between studies . CONCLUSIONS In the absence of prospect ively collected utility data , the use of mapping algorithms is feasible , however , the optimal algorithm is not clear . There were significant differences between studies , which highlight the need for validation of these algorithms in specific setting s. If cost analyses incorporate mapping algorithms to obtain utility estimates , investigators should take the variability into account BACKGROUND Conventionally , parametric models were used for health state valuation data . Recently , research ers started to explore the use of nonparametric Bayesian methods in this area . OBJECTIVES We present a nonparametric Bayesian model to estimate a preference-based index for two condition-specific five-dimensional health state classifications , one for asthma ( five-dimensional Asthma Quality of Life Utility Index ) and the other for overactive bladder ( five-dimensional Overactive Bladder Quality of Life-Utility Index ) . METHODS Sample s of 307 and 311 members of the UK general population valued 99 health states selected from a total of 3125 health states defined by each of the measures using the time trade-off technique . The article presents the results of the nonparametric model and compares it with the original model estimated using a conventional parametric r and om-effects model . The different methods are compared theoretically and in terms of empirical performance across the two data sets . It also reports the effect of respondent characteristics on health state valuations . RESULTS The nonparametric models were found to be better at predicting health state values within the estimation sample than without in terms of root mean square error and the patterns of st and ardized residuals . Some respondent characteristics were found to explain variation in health state values , but these did not have a significant effect on the health states values when estimates were adjusted for sample differences with the general population . CONCLUSIONS The nonparametric Bayesian models are theoretically more appropriate than previously used parametric models and provide better utility estimates from the two condition-specific measures . Furthermore , the model is more flexible in estimating the effect of covariates Purpose This study aim ed to develop a utility index ( the ABC-UI ) from the Aberrant Behavior Checklist-Community ( ABC-C ) , for use in quantifying the benefit of emerging treatments for fragile X syndrome ( FXS ) . Methods The ABC-C is a proxy-completed assessment of behaviour and is a widely used measure in FXS . A subset of ABC-C items across seven dimensions was identified to include in health state descriptions . This item reduction process was based on item performance , factor analysis and Rasch analysis performed on an observational study data set , and consultation with five clinical experts and a method ological expert . Dimensions were combined into health states using an orthogonal design and valued using time trade-off ( TTO ) , with lead-time TTO methods used where TTO indicated a state valued as worse than dead . Preference weights were estimated using mean , individual level , ordinary least squares and r and om-effects maximum likelihood estimation [ RE ( MLE ) ] regression models . Results A representative sample of the UK general public ( n = 349 ; mean age 35.8 years , 58.2 % female ) each valued 12 health states . Mean observed values ranged from 0.92 to 0.16 for best to worst health states . The RE ( MLE ) model performed best based on number of significant coefficients and mean absolute error of 0.018 . Mean utilities predicted by the model covered a similar range to that observed . Conclusions The ABC-UI estimates a wide range of utilities from patient-level FXS ABC-C data , allowing estimation of FXS health-related quality of life impact for economic evaluation from an established FXS clinical trial instrument Background Many intervention-based studies aim ing to improve mental health do not include a multi-attribute utility instrument ( MAUI ) that produces quality -adjusted life-years ( QALYs ) and it limits the applicability of the health economic analyses . This study aims to develop ‘ crosswalk ’ transformation algorithm between a measure for psychological distress General Health Question naire ( GHQ-12 ) and MAUI EuroQoL ( EQ-5D-3L ) . Methods The study is based on a survey question  naire sent to a r and om sample in four counties in Sweden in 2012 . The survey included  GHQ-12 and EQ-5D instruments  , as well as a question about self-rated health . The EQ-5D index was calculated using the UK and the Swedish tariff values . Two OLS models were used to estimate the EQ-5D health state values using the GHQ-12 as exposure , based on the respondents ( n = 17 , 101 ) of two counties . The algorithms were applied to the data from two other counties , ( n = 15 , 447 ) to check the predictive capacity of the models . Results The final models included gender , age , self-rated health and GHQ-12 scores as a quantitative variable . The regression equations explained 40 % ( UK tariff ) and 46 % ( Swedish tariff ) of the variances . The model showed a satisfying predictive capacity between the observed and the predicted EQ-5D index score , with Pearson correlation = 0.65 and 0.69 for the UK and Swedish models , respectively . Conclusion The algorithms developed in this study can be used to determine cost-effectiveness of services or interventions that use GHQ-12 as a primary outcome where the utility measures are not collected Background : Non – small-cell lung cancer ( NSCLC ) has a significant impact on patients ’ health-related quality of life ( HRQOL ) . This study aim ed to measure health state utility values representing the individual ’s preferences for specific health-related outcomes in advanced NSCLC patients and to assess predictive parameters . Methods : We conducted a prospect i ve  quality -of-life survey on advanced NSCLC patients in 25 hospitals in Europe , Canada , Australia , and Turkey .  HRQOL was assessed using the  EuroQol ( EQ-5D ) question naire and  EQ-5D utility  and  EQ-visual analog ( EQ-VAS ) scores were estimated . Results : Three hundred nineteen patients were recruited of which 263 had evaluable data .  Mean utility for progression-free ( PF ) patients on first- , second- , and third-/fourth-line treatment was 0.71 ( SD = 0.24 ) , 0.74 ( SD = 0.18 ) , and 0.62 ( SD = 0.29 ) , respectively . Mean utility for patients with progressive disease ( PD ) while on first- , second- and third-/fourth-line treatment was 0.67 ( SD = 0.2 ) , 0.59 ( SD = 0.34 ) , and 0.46 ( SD = 0.38 ) , respectively . Overall , patients with PD had lower mean utility scores than PF patients ( 0.58 versus 0.70 ) . The results of the EQ-VAS showed that the score decreased with later treatment lines . Patients with PD had a 10-point decrease in VAS scores compared with PF patients ( 53.7 versus 66.6 ) . The regression analysis revealed that stage IV disease , higher lines of treatment , and health state were significant predictors of utility at the 10 % level . Conclusion : The results presented indicate a substantial impact of lung cancer on patients ’ HRQOL , with stage IV disease , line of treatment , and PD , result ing in considerable deterioration of utility . The values obtained here will inform evaluations of cost-utility for NSCLC therapies Purpose Condition-specific preference-based measures can offer utility data where they would not otherwise be available or where generic measures may lack sensitivity , although they lack comparability across conditions . This study aim ed to develop an algorithm for estimating utilities from the short bowel syndrome health-related quality of life scale ( SBS-QoL ™ ) . Methods SBS-QoL ™ items were selected based on factor and item performance analysis of a European SBS-QoL ™ data set and consultation with 3 SBS clinical experts . Six-dimension health states were developed using 8 SBS-QoL ™ items ( 2 dimensions combined 2 SBS-QoL ™ items ) . SBS health states were valued by a UK general population sample ( N = 250 ) using the lead-time time trade-off method . Preference weights or ‘ utility decrements ’ for each severity level of each dimension were estimated by regression models and used to develop the scoring algorithm . Results Mean utilities for the SBS health states ranged from −0.46 ( worst health state , very much affected on all dimensions ) to 0.92 ( best health state , not at all affected on all dimensions ) . The r and om effects model with maximum likelihood estimation regression had the best predictive ability and lowest root mean squared error and mean absolute error , and was used to develop the scoring algorithm . Conclusions The preference-weighted scoring algorithm for the SBS-QoL ™ developed is able to estimate a wide range of utility values from patient-level SBS-QoL ™ data . This allows estimation of SBS HRQL impact for the purpose of economic evaluation of SBS treatment benefits 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 OBJECTIVES The Rol and Morris Question naire ( RMQ ) is a widely used health status measure for low back pain ( LBP ) . It is not preference-based , and there are currently no established algorithms for mapping between the RMQ and preference-based health-related quality -of-life measures . Using data from r and omized controlled trials of treatment for LBP , we sought to develop algorithms for mapping between RMQ scores and health utilities derived using either the EuroQol five-dimensional question naire ( EQ-5D ) or the six-dimensional health state short form ( derived from Medical Outcomes Study 36-Item Short-Form Health Survey ) ( SF-6D ) . METHODS This study is based on data from the Back Skills Training Trial in which data were collected from 701 patients at baseline and subsequently at 3 , 6 , and 12 months postr and omization using a range of outcome measures , including the  RMQ , the EQ-5D , and the Short Form 12 item Health Survey ( SF-12 ) ( from which SF-6D utilities can be derived ) . We used baseline trial data to estimate models using both direct and response mapping approaches to predict EQ-5D and SF-6D health utilities and dimension responses . A multistage model selection process was used to assess the predictive accuracy of the models . We then explored different techniques and mapping models that made use of repeated follow-up observations in the data . The estimated mapping algorithms were vali date d using external data from the UK Back Pain Exercise and Manipulation trial . RESULTS A number of models were developed that accurately predict health utilities in this context . The best performing model for RMQ to EQ-5D mapping was a beta regression with Bayesian quasi-likelihood estimation that included 24 dummy variables for RMQ responses , age , and sex as covariates ( mean squared error 0.0380 ) based on repeated data . The model selected for RMQ to SF-6D mapping was a finite mixture model that included the overall RMQ score , age , sex , RMQ score squared , age squared , and an interaction term for age and RMQ score as covariates ( mean squared error 0.0114 ) based on repeated data . CONCLUSIONS It is possible to reasonably predict EQ-5D and SF-6D health utilities from RMQ scores and responses using regression methods . Our regression equations provide an empirical basis for estimating health utilities when EQ-5D or SF-6D data are not available . They can be used to inform future economic evaluations of interventions targeting LBP In contrast to the recent proliferation of studies incorporating ordinal methods to generate health state values from adults , to date relatively few studies have utilised ordinal methods to generate health state values from adolescents . This paper reports upon a study to apply profile case best worst scaling methods to derive a new adolescent specific scoring algorithm  for the Child Health Utility 9D ( CHU9D ) , a generic preference based instrument that has been specifically design ed for the estimation of quality adjusted life years for the economic evaluation of health care treatment and preventive programs targeted at young people . A survey was developed for administration in an on-line format in which consenting community based Australian adolescents aged 11 - 17 years ( N = 1982 ) indicated the best and worst features of a series of 10 health states derived from the CHU9D descriptive system . The data were analyzed using latent class conditional logit models to estimate values ( part worth utilities ) for each level of the nine attributes relating to the CHU9D . A marginal utility matrix was then estimated to generate an adolescent-specific scoring algorithm  on the full health = 1 and dead = 0 scale required for the calculation of QALYs . It was evident that different decision processes were being used in the best and worst choices . Whilst respondents appeared readily able to choose ' best ' attribute levels for the CHU9D health states , a large amount of r and om variability and indeed different decision rules were evident for the choice of ' worst ' attribute levels , to the extent that the best and worst data should not be pooled from the statistical perspective . The optimal  adolescent-specific scoring algorithm  was therefore derived using data obtained from the best choices only . The study provides important insights into the use of profile case best worst scaling methods to generate health state values with adolescent population Background Chronic obstructive pulmonary disease ( COPD ) is associated with increased mortality and poor health-related quality of life ( HRQoL ) compared with the general population . The objective of this study was to identify clinical characteristics which predict mortality and very poor HRQoL among the COPD population and to develop a Bayesian prediction model . Methods The data consisted of 738 patients with COPD who had visited the Pulmonary Clinic of the Helsinki and Turku University Hospitals during 1995–2006 . The data set contained 49 potential predictor variables and two outcome variables : survival ( dead/alive ) and HRQoL measured with a 15D instrument  ( very poor HRQoL < 0.70 vs. typical HRQoL ≥ 0.70).In the first phase of model validation we r and omly divided the material into a training set ( n = 538 ) , and a test set ( n = 200 ) . This procedure was repeated ten times in r and om fashion to obtain independently created training sets and corresponding test sets . Modeling was performed by using the training set , and each model was tested by using the corresponding test set , repeated in each training set . In the second phase the final model was created by using the total material and eighteen most predictive variables . The performance of six logistic regressions approaches were shown for comparison purpose s. Results In the final model , the following variables were associated with mortality or very poor HRQoL : age at onset , cerebrovascular disease , diabetes , alcohol abuse , cancer , psychiatric disease , body mass index , Forced Expiratory Volume ( FEV1 ) % of predicted , atrial fibrillation , and prolonged QT time in ECG . The prediction accuracy of the model was 77 % , sensitivity 0.30 , specificity 0.95 , positive predictive value 0.68 , negative predictive value 0.78 , and area under the ROC curve 0.69 . While the sensitivity of the model reminded limited , good specificity , moderate accuracy , comparable or better performance in classification and better performance in variable selection and data usage in comparison to the logistic regression approaches , and positive and negative predictive values indicate that the model has potential in predicting mortality and very poor HRQoL in COPD patients . Conclusion We developed a Bayesian prediction model which is potentially useful in predicting mortality and very poor HRQoL in patients with COPD Purpose The purpose of the study was to compare alternative statistical techniques to find the best approach for converting QLQ-C30 scores onto EQ-5D-5L and SF-6D utilities , and to estimate the mapping algorithms that best predict these health state utilities . Methods 772 cancer patients described their health along the  cancer-specific instrument ( QLQ-C30 )  and two generic preference-based instruments ( EQ-5D-5L and SF-6D ) . Seven alternative regression models were applied : ordinary least squares , generalized linear model , extended estimating equations ( EEE ) , fractional regression model , beta binomial ( BB ) regression , logistic quantile regression and censored least absolute deviation .  Normalized mean absolute error ( NMAE ) , normalized root mean square error ( NRMSE ) , r-squared ( r2 ) and concordance correlation coefficient ( CCC ) were used as model performance criteria . Cross-validation was conducted by r and omly splitting internal data set into two equally sized groups to test the generalizability of each model . Results In predicting EQ-5D-5L utilities , the BB regression performed best . It gave better predictive accuracy in terms of all criteria in the full sample , as well as in the validation sample . In predicting SF-6D , the EEE performed best . It outperformed in all criteria : NRMSE = 0.1004 , NMAE = 0.0798 , CCC = 0.842 and r2 = 72.7 % in the full sample , and NRMSE = 0.1037 , NMAE = 0.0821 , CCC = 0.8345 and r2 = 71.4 % in cross-validation . Conclusions When only QLQ-C30 data are available , mapping provides an alternative approach to obtain health state utility data for use in cost-effectiveness analyses . Among seven alternative regression models , the BB and the EEE gave the most accurate predictions for EQ-5D-5L and SF-6D , respectively
Conclusions The last 10 years has seen a substantial increase in the number of mapping studies and some evidence of advancement in methods with consideration of models beyond OLS and greater reporting of predictive ability of mapping functions
Background Mapping is an increasingly common method used to predict instrument-specific preference-based health-state utility values ( HSUVs ) from data obtained from another health-related quality of life ( HRQoL ) measure .
There have been several method ological developments in this area since a previous review up to 2007 .