human
***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: 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 Purpose It is not always possible to collect utility-based outcome data , like EQ-5D , needed for conducting economic evaluations in population s of older people . Sometimes , information on other non-utility outcome measures may have been collected . This paper examines the possibility of mapping the EQ-5D from a non-utility-based outcome , the Barthel index . Methods Data for 1,189 UK intermediate care patients were used .  Ordinary least squares ( OLS ) , censored least absolute deviations ( CLAD ) estimator and multinomial logistic ( ML ) models were used . The mean absolute error ( MAE ) and root-mean-squared error ( RMSE ) were used to estimate the predictive accuracy of eight regression models . Validation of primary models was carried out on r and om sample s of data collected at admission and discharge . Results Models where the EQ-5D was entered as a continuous dependent variable and Barthel dimensions used as explanatory variables performed better . CLAD performed best on MAE and OLS on the RMSE , while the ML performed the worst on both measures . The CLAD predicted EQ-5D scores that matched the observed values more closely than the OLS . Conclusions It is possible to reasonably predict that the EQ-5D from the Barthel using regression methods and the CLAD model ( 4 ) is recommended 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 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 . SF-6D utility weights are conventionally produced using a st and ard gamble ( SG ) . SG-derived weights consistently demonstrate a floor effect not observed with other elicitation techniques . Recent advances in discrete choice methods have allowed estimation of utility weights . The objective was to produce Australian utility weights for the SF-6D and to explore the application of discrete choice experiment ( DCE ) methods in this context . We hypothesized that weights derived using this method would reflect the largely monotonic construction of the SF-6D . Methods . We design ed an online DCE and administered it to an Australia-representative online panel ( n = 1017 ) . A range of specifications investigating nonlinear preferences with respect to additional life expectancy were estimated using a r and om-effects probit model . The preferred model was then used to estimate a preference index such that full health and death were valued at 1 and 0 , respectively , to provide an algorithm for Australian cost-utility analyses . Results . Physical functioning , pain , mental health , and vitality were the largest drivers of utility weights . Combining levels to remove illogical orderings did not lead to a poorer model fit . Relative to international SG-derived weights , the range of utility weights was larger with 5 % of health states valued below zero . Conclusion s. DCEs can be used to investigate preferences for health profiles and to estimate utility weights for multi-attribute utility instruments . Australian cost-utility analyses can now use domestic SF-6D weights . The comparability of DCE results to those using other elicitation methods for estimating utility weights for quality -adjusted life-year calculations should be further investigated The Health Utilities Index is a generic multiattribute preference-based system for assessing health-related quality of life , devised by Torrance et al. It is being used in cost-effectiveness evaluations in North America and in international multicentre studies but was not available in France . Following adaptation of the HUI3 classification in France , the purpose of the reported investigation was to derive French preference weights . This article provides a reminder of the theoretical foundations used to model the multiattribute utility function . Within this framework , a multiattribute multiplicative aggregate utility function was constructed in accordance with the explicitly decomposed approach . The study took place in June 1999 over a sample of 365 persons from the French general population , aged between 20 and 65 , and not suffering from any chronic or incapacitating illness . The recruitment procedure was based upon a r and om selection of individuals , using the phone book . Interviews took place in the homes of the interviewees . The methods of revelation ( VAS and SG ) were applied by setting the value of the best possible state of the HUI3 a priori at 1 , and leaving a choice between two states ( worst possible state , death ) for 0 . The aggregated individuals ( person-mean and median ) were calculated and the multiplicative utility functions constructed . A comparison of the calculated utilities with the observed ones provides a primary indicator of the validity of the person-mean or median functions constructed . The slight absolute differences obtained between observed and calculated utilities and the low RMSE scores lead us towards a favourable conclusion RATIONALE , AIMS , AND OBJECTIVES Although nonpreference-based disease-specific measures are widely used in clinical studies , they can not generate utilities for economic evaluation . A solution to this problem is to estimate utilities from disease-specific instruments using the mapping function . This study aim ed to develop a transformation model for mapping the pruritus-visual analog scale ( VAS ) to the EuroQol 5-Dimension 3-Level ( EQ-5D-3L ) utility index in pruritus . METHODS A cross-sectional survey was conducted with a sample ( n = 268 ) drawn from the general population of South Korea . Data were r and omly divided into 2 groups , one for estimating and the other for validating mapping models . To select the best model , we developed and compared 3 separate models using demographic information and the pruritus-VAS as independent variables . The predictive performance was assessed using the mean absolute deviation and root mean square error in a separate data set . RESULTS Among the 3 models , model 2 using age , age squared , sex , and the pruritus-VAS as independent variables had the best performance based on the goodness of fit and model simplicity , with a log likelihood of 187.13 . The 3 models had similar precision errors based on mean absolute deviation and root mean square error in the validation data set . No statistically significant difference was observed between the mean observed and predicted values in all models . CONCLUSIONS In conclusion , model 2 was chosen as the preferred mapping model . Outcomes measured as the pruritus-VAS can be transformed into the EQ-5D-3L utility index using this mapping model , which makes an economic evaluation possible when only pruritus-VAS data are available 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 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 Study Design . Cross-sectional analysis of the Cervical Spine Research Society Outcomes Study , a longitudinal multicenter cohort study . Objective . To provide a statistical model for estimating health utility from the Cervical Spine Outcomes Question naire ( CSOQ ) domain scores . Summary of Background Data . There is consensus among health-policy research ers that the results of economic evaluations of health can be reported in quality -adjusted life-years ( QALY ) . The CSOQ is a condition-specific instrument that assesses individuals across several domains , including neck and arm/ 
    Output:
    
agent
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
***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: 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 Purpose It is not always possible to collect utility-based outcome data , like EQ-5D , needed for conducting economic evaluations in population s of older people . Sometimes , information on other non-utility outcome measures may have been collected . This paper examines the possibility of mapping the EQ-5D from a non-utility-based outcome , the Barthel index . Methods Data for 1,189 UK intermediate care patients were used .  Ordinary least squares ( OLS ) , censored least absolute deviations ( CLAD ) estimator and multinomial logistic ( ML ) models were used . The mean absolute error ( MAE ) and root-mean-squared error ( RMSE ) were used to estimate the predictive accuracy of eight regression models . Validation of primary models was carried out on r and om sample s of data collected at admission and discharge . Results Models where the EQ-5D was entered as a continuous dependent variable and Barthel dimensions used as explanatory variables performed better . CLAD performed best on MAE and OLS on the RMSE , while the ML performed the worst on both measures . The CLAD predicted EQ-5D scores that matched the observed values more closely than the OLS . Conclusions It is possible to reasonably predict that the EQ-5D from the Barthel using regression methods and the CLAD model ( 4 ) is recommended 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 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 . SF-6D utility weights are conventionally produced using a st and ard gamble ( SG ) . SG-derived weights consistently demonstrate a floor effect not observed with other elicitation techniques . Recent advances in discrete choice methods have allowed estimation of utility weights . The objective was to produce Australian utility weights for the SF-6D and to explore the application of discrete choice experiment ( DCE ) methods in this context . We hypothesized that weights derived using this method would reflect the largely monotonic construction of the SF-6D . Methods . We design ed an online DCE and administered it to an Australia-representative online panel ( n = 1017 ) . A range of specifications investigating nonlinear preferences with respect to additional life expectancy were estimated using a r and om-effects probit model . The preferred model was then used to estimate a preference index such that full health and death were valued at 1 and 0 , respectively , to provide an algorithm for Australian cost-utility analyses . Results . Physical functioning , pain , mental health , and vitality were the largest drivers of utility weights . Combining levels to remove illogical orderings did not lead to a poorer model fit . Relative to international SG-derived weights , the range of utility weights was larger with 5 % of health states valued below zero . Conclusion s. DCEs can be used to investigate preferences for health profiles and to estimate utility weights for multi-attribute utility instruments . Australian cost-utility analyses can now use domestic SF-6D weights . The comparability of DCE results to those using other elicitation methods for estimating utility weights for quality -adjusted life-year calculations should be further investigated The Health Utilities Index is a generic multiattribute preference-based system for assessing health-related quality of life , devised by Torrance et al. It is being used in cost-effectiveness evaluations in North America and in international multicentre studies but was not available in France . Following adaptation of the HUI3 classification in France , the purpose of the reported investigation was to derive French preference weights . This article provides a reminder of the theoretical foundations used to model the multiattribute utility function . Within this framework , a multiattribute multiplicative aggregate utility function was constructed in accordance with the explicitly decomposed approach . The study took place in June 1999 over a sample of 365 persons from the French general population , aged between 20 and 65 , and not suffering from any chronic or incapacitating illness . The recruitment procedure was based upon a r and om selection of individuals , using the phone book . Interviews took place in the homes of the interviewees . The methods of revelation ( VAS and SG ) were applied by setting the value of the best possible state of the HUI3 a priori at 1 , and leaving a choice between two states ( worst possible state , death ) for 0 . The aggregated individuals ( person-mean and median ) were calculated and the multiplicative utility functions constructed . A comparison of the calculated utilities with the observed ones provides a primary indicator of the validity of the person-mean or median functions constructed . The slight absolute differences obtained between observed and calculated utilities and the low RMSE scores lead us towards a favourable conclusion RATIONALE , AIMS , AND OBJECTIVES Although nonpreference-based disease-specific measures are widely used in clinical studies , they can not generate utilities for economic evaluation . A solution to this problem is to estimate utilities from disease-specific instruments using the mapping function . This study aim ed to develop a transformation model for mapping the pruritus-visual analog scale ( VAS ) to the EuroQol 5-Dimension 3-Level ( EQ-5D-3L ) utility index in pruritus . METHODS A cross-sectional survey was conducted with a sample ( n = 268 ) drawn from the general population of South Korea . Data were r and omly divided into 2 groups , one for estimating and the other for validating mapping models . To select the best model , we developed and compared 3 separate models using demographic information and the pruritus-VAS as independent variables . The predictive performance was assessed using the mean absolute deviation and root mean square error in a separate data set . RESULTS Among the 3 models , model 2 using age , age squared , sex , and the pruritus-VAS as independent variables had the best performance based on the goodness of fit and model simplicity , with a log likelihood of 187.13 . The 3 models had similar precision errors based on mean absolute deviation and root mean square error in the validation data set . No statistically significant difference was observed between the mean observed and predicted values in all models . CONCLUSIONS In conclusion , model 2 was chosen as the preferred mapping model . Outcomes measured as the pruritus-VAS can be transformed into the EQ-5D-3L utility index using this mapping model , which makes an economic evaluation possible when only pruritus-VAS data are available 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 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 Study Design . Cross-sectional analysis of the Cervical Spine Research Society Outcomes Study , a longitudinal multicenter cohort study . Objective . To provide a statistical model for estimating health utility from the Cervical Spine Outcomes Question naire ( CSOQ ) domain scores . Summary of Background Data . There is consensus among health-policy research ers that the results of economic evaluations of health can be reported in quality -adjusted life-years ( QALY ) . The CSOQ is a condition-specific instrument that assesses individuals across several domains , including neck and arm/ 
    Output:
    