A systematic review of the validity and responsiveness of EQ-5D and SF-6D for depression and anxiety

Background: Generic preference based measures (PBM) such as the SF-6D and EQ-5D are increasingly used to inform health care resource allocation decisions. They aim to be generic in the sense of being applicable to all physical and mental health conditions. However, their applicability has not been demonstrated for all mental health conditions. Aims: To assess the construct validity and responsiveness of EQ-5D and SF-6D measures in depression and anxiety. Method: A systematic review of the literature was undertaken. Eleven databases were searched in December 2010 and reference lists scrutinised to identify relevant studies. Studies were appraised and data extracted. A narrative synthesis was performed of the evidence on construct validity including known groups validity (detecting a difference in PBM scores between different groups such as different levels of severity of depression), convergent validity (strength of association between generic PBM and other outcome measures) and responsiveness (the ability to detect relevant health changes in health status and the absence of change where there is none). Results: 26 studies were identified that provided data on the validity and/or responsiveness of the EQ-5D and SF-6D. Both measures demonstrate good construct validity and responsiveness for depression. One study, however, suggests EQ-5D may lack responsiveness in the elderly. These measures are more highly correlated with depression scales in patients with anxiety than they are clinical anxiety scales suggesting known group validity in patients with anxiety may be driven by aspects of depression within anxiety disorder and the presence of co-morbid depression. Direct comparisons between the measures find that the EQ-5D gives lower utility levels for severe depression hence greater health improvement for this group and SF-6D shows more sensitivity to mild depression and performs better in terms of ES and SRM. The comparison between EQ-5D and SF-6D is similar to that found in other conditions. Conclusion: The evidence base supports the use of EQ-5D and SF-6D in patients with depression and anxiety. More work is needed on the true utility level for severe depression

Common scale valuations across different preference-based measures: estimation using rank data

Background: Different preference-based measures (PBMs) used to estimate Quality Adjusted Life Years (QALYs) provide di¤erent utility values for the same patient. Differences are expected since values have been obtained using different samples, valuation techniques and descriptive systems. Previous studies have estimated the relationship between pairs of PBMs using patient self-reported data. However, there is a need for an approach capable of generating values directly on a common scale for a range of PBMs using the same sample of general population respondents and valuation technique but keeping the advantages of the different descriptive systems. Methods: General public survey data (n=501) where respondents ranked health states described using subsets of six PBMs were analysed. We develop a new model based on the mixed logit to overcome two key limitations of the standard rank ordered logit model, namely, the unrealistic choice pattern (Independence of Irrelevant Alternatives) and the independence of repeated observations. Results: There are substantial differences in the estimated parameters between the two models (mean di¤erence 0.07) leading to di¤erent orderings across the measures. Estimated values for the best states described by di¤erent PBMs are substantially and significantly di¤erent using the standard model, unlike our approach which yields more consistent results. Limitations: Data come from a exploratory study that is relatively small both in sample size and coverage of health states. Conclusions: This study develops a new, �exible econometric model specifically designed to reflect appropriately the features of rank data. Results support the view that the standard model is not appropriate in this setting and will yield very different and apparently inconsistent results. PBMs can be compared using a common scale by implementation of this new approach

Using Rasch analysis to form plausible health states amenable to valuation: the development of CORE-6D from CORE-OM in order to elicit preferences for common mental health problems

Purpose: To describe a new approach for deriving a preference-based index from a condition specific measure that uses Rasch analysis to develop health states. Methods: CORE-OM is a 34-item instrument monitoring clinical outcomes of people with common mental health problems. CORE-OM is characterised by high correlation across its domains. Rasch analysis was used to reduce the number of items and response levels in order to produce a set of unidimensionally-behaving items, and to generate a credible set of health states corresponding to different levels of symptom severity using the Rasch item threshold map. Results: The proposed methodology resulted in the development of CORE-6D, a 2-dimensional health state description system consisting of a unidimensionally-behaving 5-item emotional component and a physical symptom item. Inspection of the Rasch item threshold map of the emotional component helped identify a set of 11 plausible health states, which, combined with the physical symptom item levels, will be used for the valuation of the instrument, resulting in the development of a preference-based index. Conclusions: This is a useful new approach to develop preference-based measures where the domains of a measure are characterised by high correlation. The CORE-6D preference-based index will enable calculation of Quality Adjusted Life Years in people with common mental health problems

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

It's all in the name, or is it? The impact of labelling on health state values

Many descriptions of health used in vignettes and condition-specific measures refer to the medical condition. This paper assesses the impact of referring to the medical condition in the descriptions of health states valued by members of the general population. A sample of 241 members of the UK general population each valued 8 health states using time trade-off. All respondents valued essentially the same health states, but for each respondent the descriptions featured either an irritable bowel syndrome label, a cancer label or no label. Regression techniques were used to estimate the impact of each label and experience of the condition on health state values. We find that the inclusion of a cancer label in health state descriptions affects health state values and that the impact is dependent upon the severity of the state. A condition label can affect health state values, but this is dependent upon the specific condition and severity. It is recommended to avoid condition labels in health state descriptions (where possible) to ensure that values are not affected by prior knowledge or preconception of the condition that may distort the health state being valued

Deriving preference-based single indices from non-preference based condition-specific instruments: converting AQLQ into EQ5D indices

Suppose that one has a clinical dataset with only non-preference-based QOL data, and that one nevertheless would like to perform a cost/QALY analysis. This study reports on some efforts to establish a “mapping” relationship between AQLQ (a non-preference-based QOL instrument for asthma) and EQ5D (a preference-based generic instrument). Various methods are described in terms of associated assumptions regarding the measurement properties of the instruments. This is followed by empirical mapping, based on regressing EQ5D on AQLQ. Six main regression models and two supplementary models are identified, and the regressions carried out. Performance of each model is explored in terms of goodness of fit between observed and predicted values, and of robustness of predictions on external data. The results show that it is possible to predict mean EQ5D indices given AQLQ data. The general implications for methods of mapping non-preference-based instruments onto preference-based measures are discussed.EQ5D; AQLQ; mapping

Using rank and discrete choice data to estimate health state utility values on the QALY scale

Objective: Recent years has seen increasing interest in the use of ordinal methods to elicit health state utility values as an alternative to conventional methods such as standard gamble and time trade-off. However, in order to use these health state values in cost effectiveness analysis using cost per quality adjusted life year (QALY) analysis, these values must be anchored on the full health-dead scale. This study addresses this challenge and examines how rank and discrete choice experiment data can be used to elicit health state utility values anchored on the full health-dead scale and compares the results to time trade-off (TTO) results. Methods: Two valuation studies were conducted using identical methods for two health state classification systems: asthma and overactive bladder. Each valuation study involved interviews of 300 members of the general population using ranking and TTO plus a postal survey using discrete choice experiment sent to all consenting interviewees and a "cold" sample of the general population who were not interviewed. Results: Overall DCE produced different results to ranking and time trade-off, whereas ranking produced similar results to TTO in one study, but not the other. Conclusions: Ordinal methods offer a promising alternative to conventional cardinal methods of standard gamble and TTO. However, the results do not appear to be robust across different health state classification systems and potentially different medical conditions. There remains a large and important research agenda to address

HEDS Discussion Paper 09-15: Developing preference-based health measures: using Rasch analysis to generate health state values

Background/aims: Condition specific measures may not always have independent items, and existing techniques of developing health state values from these measures are inappropriate when items are not independent. This study develops methods for deriving and valuing health states for a preference-based measure. Methods: Three key stages are presented: Rasch analysis is used to develop a health state classification system and identify a set of health states for valuation. A valuation survey of the health states using time-trade-off (TTO) methods is conducted to elicit health state values. Finally, regression models are applied to map the relationship between mean TTO values and Rasch logit values. The model is then used to estimate health state values for all possible health states. Methods are illustrated using the Flushing Symptoms Questionnaire (FSQ). Results: Rasch models were fitted to 1270 responders to the FSQ and a series of 16 health states identified for the valuation exercise. An ordinary least squares model best described the relationship between mean TTO values and Rasch logit values. (R2 = 0.958; Root mean square error = 0.042). Conclusions: We have shown how the valuation of health states can be mapped onto the Rasch scale in order to value all states defined by the FSQ. This should significantly enhance work in this field

Estimating a preference-based index from the Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM): valuation of CORE-6D

Background: The Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM) is used to evaluate the effectiveness of psychological therapies in people with common mental disorders. The objective of this study was to estimate a preference-based index for this population using CORE-6D, a health state classification system derived from CORE-OM consisting of a 5-item emotional component and a physical item, and to demonstrate a novel method for generating states that are not orthogonal. Methods: Rasch analysis was used to identify 11 plausible ‘emotional’ health states from CORE-6D (rather than conventional statistical design that would generate implausible states). By combining these with the 3 response levels of the physical item of CORE-6D, 33 plausible health states can be described, of which 18 were selected for valuation. An interview valuation survey of 220 members of public in South Yorkshire, UK, was undertaken using the time-trade-off method to value the 18 health states; regression analysis was subsequently used to predict values for all possible states described by CORE-6D. Results: A number of multivariate regression models were built to predict values for the 33 plausible health states of CORE-6D, using the Rasch logit value of the emotional health state and the response level of the physical item as independent variables. A cubic model with high predictive value (adjusted R squared 0.990) was finally selected, which can be used to predict utility values for all 927 states described by CORE-6D. Conclusion: The CORE-6D preference-based index will enable the assessment of cost-effectiveness of interventions for people with common mental disorders using existing and prospective CORE-OM datasets. The new method for generating states may be useful for other instruments with highly correlated dimensions