Mapping Functions in Health-Related Quality of Life: Mapping From Two Cancer-Specific Health-Related Quality-of-Life Instruments to EQ-5D-3L.

BACKGROUND: Clinical trials in cancer frequently include cancer-specific measures of health but not preference-based measures such as the EQ-5D that are suitable for economic evaluation. Mapping functions have been developed to predict EQ-5D values from these measures, but there is considerable uncertainty about the most appropriate model to use, and many existing models are poor at predicting EQ-5D values. This study aims to investigate a range of potential models to develop mapping functions from 2 widely used cancer-specific measures (FACT-G and EORTC-QLQ-C30) and to identify the best model. METHODS: Mapping models are fitted to predict EQ-5D-3L values using ordinary least squares (OLS), tobit, 2-part models, splining, and to EQ-5D item-level responses using response mapping from the FACT-G and QLQ-C30. A variety of model specifications are estimated. Model performance and predictive ability are compared. Analysis is based on 530 patients with various cancers for the FACT-G and 771 patients with multiple myeloma, breast cancer, and lung cancer for the QLQ-C30. RESULTS: For FACT-G, OLS models most accurately predict mean EQ-5D values with the best predicting model using FACT-G items with similar results using tobit. Response mapping has low predictive ability. In contrast, for the QLQ-C30, response mapping has the most accurate predictions using QLQ-C30 dimensions. The QLQ-C30 has better predicted EQ-5D values across the range of possible values; however, few respondents in the FACT-G data set have low EQ-5D values, which reduces the accuracy at the severe end. CONCLUSIONS: OLS and tobit mapping functions perform well for both instruments. Response mapping gives the best model predictions for QLQ-C30. The generalizability of the FACT-G mapping function is limited to populations in moderate to good health

A review of generic preference-based measures of health-related quality of life in visual disorders

Purpose: This review examines generic preference based measures and their ability to reflect health related quality of life in patients with visual disorders. Methods: A systematic search was undertaken to identify clinical studies of patients with visual disorders where health state utility values (HSUVs) were measured and reported. Data were extracted to assess the validity and responsiveness of the measures. A narrative synthesis of the data was undertaken due to the heterogeneity between different studies. Results: There was considerable heterogeneity in the 31 studies identified in terms of patient characteristics, visual disorders and outcomes reported. Vision loss was associated with a reduction in scores across the preference-based measure, but the evidence on validity and responsiveness was mixed. The EQ-5D’s performance differed according to condition, with poor performance in age-related macular degeneration and diabetic retinopathy. The more limited evidence on the HUI-3 found it performed best in differentiating between severity groups of patients with glaucoma, AMD, cataracts and diabetic retinopathy. One study reported data on the SF-6D and showed it was able to differentiate between patients with AMD. Conclusion: The performance of the EQ-5D in visual disorders was mixed. The HUI-3 seemed to perform better in some conditions, but the evidence on this and SF-6D is limited. More head to head comparisons of these three measures are required. The new 5-level version of EQ-5D may do better at the milder end of visual function and there is research being undertaken into adding a vision relevant dimension

Reforming the cancer drug fund focus on drugs that might be shown to be cost effective

The Cancer Drug Fund was originally conceived as a temporary measure, until value based pricing for drugs was introduced, to give NHS cancer patients access to drugs not approved by NICE. Spending on these drugs rose from less than the £50m (€63m; $79m) budgeted for the first year in 2010-11 to well over £200m in 2013-14, and the budget for the scheme—now extended for a further two years—will reach £280m by 2016.1 The recent changes to the fund recognise the impossibility, within any sensible budget limit, of providing all the new cancer drugs that offer possible benefit to patients. More radical changes are needed to the working of the fund, given the failure to introduce value based pricing, so that it deals with the underlying problem of inadequate information on the effectiveness and cost effectiveness of new cancer drugs when used in the NHS

Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: systematic review, statistical modelling and survey.

© Queen’s Printer and Controller of HMSO 2014Background: The National Institute for Health and Care Excellence recommends the use of generic preference-based measures (GPBMs) of health for its Health Technology Assessments (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. Design: The project was conducted in three stages: (1) A systematic review of the psychometric properties of three commonly used GPBMs [EQ-5D, SF-6D and Health Utilities Index Mark 3 (HUI3)] in four broadly defined conditions: visual impairment, hearing impairment, cancer and skin conditions. (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 Questionnaire 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. (3) Three potential extensions to the EQ-5D are developed as ‘bolt-on’ items relating to hearing, tiredness and vision. They are valued using the time trade-off method. A second valuation study is conducted to fully value the EQ-5D with and without the vision bolt-on item in an additional sample of 300 people. Main outcome measures: Comparisons of EQ-5D, SF-6D and HUI3 in four conditions with various generic and condition-specific measures. Mapping functions were estimated between EORTC QLQ-C30 and FACT-G with EQ-5D. Three bolt-ons to the EQ-5D were developed: EQ + hearing/vision/tiredness. A full valuation study was conducted for the EQ + vision. 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. There were limited data for SF-6D in all four conditions and limited evidence on reliability of all instruments. (2) Mapping algorithms were estimated to predict EQ-5D values from alternative cancer-specific measures of health. 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. In the full analysis for the FACT-G, linear regression using ordinary least squares gave the best predictions followed by the tobit 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. Conclusions: 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. More primary research into the psychometric properties of the generic preference-based measures is required, particularly in cancer and for the assessment of reliability. Further research is needed for the development and valuation of bolt-ons to EQ-5D.UK Medical Research Council (MRC) as part of the MRC-NIHR methodology research programme (reference G0901486

Estimation of Health State Utility Values in Fabry Disease Using Vignette Development and Valuation

Background: Health state utilities are measures of health-related quality of life that reflect the value placed on improvements in patients' health status and are necessary for estimation of quality-adjusted life-years. Health state utility data on Fabry disease (FD) are limited. In this study we used vignette (scenario) construction and valuation to develop health state utilities. Objectives: The aim of this study was to use vignette construction and valuation to estimate health state utility values suitable for inclusion in economic models of FD treatments. Methods: Health state vignettes were developed from semistructured qualitative telephone interviews with patients with FD and informed by published literature and input from an expert. Each vignette was valued in an online survey by members of the United Kingdom (UK) general population using the composite time trade-off (TTO) method, which aims to determine the time the respondent would trade to live in full health compared with each impaired health state. Results: Eight adults (50% women) with FD from the UK were interviewed. They were recruited via various approaches, including patient organizations and social media. The interviewees' responses, evidence from published literature, and input from a clinical expert informed the development of 6 health state vignettes (pain, moderate clinically evident FD [CEFD], severe CEFD, end-stage renal disease [ESRD], stroke, and cardiovascular disease [CVD]) and 3 combined health states (severe CEFD + ESRD, severe CEFD + CVD, and severe CEFD + stroke). A vignette valuation survey was administered to 1222 participants from the UK general population who were members of an external surveying organization and agreed to participate in this study; 1175 surveys were successfully completed and included in the analysis. Responses to TTO questions were converted into utility values for each health state. Pain was the highest valued health state (0.465), and severe CEFD + ESRD was the lowest (0.033). Discussion: Overall, mean utility values declined as the severity of the vignettes increased, indicating that respondents were more willing to trade life-years to avoid a severe health state. Conclusions: Health state vignettes reflect the effects of FD on all major health-related quality-of-life domains and may help to support economic modeling for treatment of FD

Preferred reporting items for studies mapping onto preference-based outcome measures: The MAPS statement

'Mapping' onto generic preference-based outcome measures is increasingly being used as a means of generating health utilities for use within health economic evaluations. Despite publication of technical guides for the conduct of mapping research, guidance for the reporting of mapping studies is currently lacking. The MAPS (MApping onto Preference-based measures reporting Standards) statement is a new checklist, which aims to promote complete and transparent reporting of mapping studies. The primary audiences for the MAPS statement are researchers reporting mapping studies, the funders of the research, and peer reviewers and editors involved in assessing mapping studies for publication. A de novo list of 29 candidate reporting items and accompanying explanations was created by a working group comprised of six health economists and one Delphi methodologist. Following a two-round, modified Delphi survey with representatives from academia, consultancy, health technology assessment agencies and the biomedical journal editorial community, a final set of 23 items deemed essential for transparent reporting, and accompanying explanations, was developed. The items are contained in a user friendly 23 item checklist. They are presented numerically and categorised within six sections, namely: (i) title and abstract; (ii) introduction; (iii) methods; (iv) results; (v) discussion; and (vi) other. The MAPS statement is best applied in conjunction with the accompanying MAPS explanation and elaboration document. It is anticipated that the MAPS statement will improve the clarity, transparency and completeness of reporting of mapping studies. To facilitate dissemination and uptake, the MAPS statement is being co-published by eight health economics and quality of life journals, and broader endorsement is encouraged. The MAPS working group plans to assess the need for an update of the reporting checklist in five years' time. This statement was published jointly in Applied Health Economics and Health Policy, Health and Quality of Life Outcomes, International Journal of Technology Assessment in Health Care, Journal of Medical Economics, Medical Decision Making, PharmacoEconomics, and Quality of Life Research