Deriving a Preference-Based Measure for People With Duchenne Muscular Dystrophy From the DMD-QoL

OBJECTIVES

This study generates a preference-based measure for capturing the quality of life of people with Duchenne muscular dystrophy (DMD) from a new measure of quality of life, DMD-QoL.

METHODS

A health state classification system was derived from the DMD-QoL based on psychometric performance of items, factor analysis, and item response theory analysis. Preferences for health states described by the classification system were elicited using an online discrete choice experiment survey with life years as an additional attribute, from members of the UK general population (n = 1043). Discrete choice experiment data was modeled using a conditional fixed-effects logit model and utility estimates were directly anchored on the 1 to 0 full health-dead scale.

RESULTS

The health state classification system has 8 dimensions: mobility, difficulty using hands, difficulty breathing, pain, tiredness, worry, participation, and feeling good about yourself. The standard model had mostly statistically significant coefficients and reflected the instrument's monotonic structure. However, 2 dimensions had inconsistent coefficients (where utility increased as health worsened) and a consistent model was estimated that merged adjacent inconsistent severity levels. The best state defined by the classification system has a value of 1 and the worst state has a value of -0.559.

CONCLUSION

The modeled results enable DMD-QoL-8D utility values to be generated using DMD-QoL or DMD-QoL-8D data to generate QALYs for people with DMD. QALYs can then be used to inform economic models of the cost-effectiveness of interventions in DMD. Future research comparing the psychometric performance of DMD-QoL-8D to existing generic preference-based measures, including EQ-5D-5L, is recommended.

Impact of social prescribing to address loneliness: A mixed methods evaluation of a national social prescribing programme

Loneliness is considered a global public health issue because of its detrimental impact on physical and mental health but little is known about which interventions can reduce loneliness. One potential intervention is social prescribing, where a link worker helps service-users to access appropriate support such as community activities and social groups. Some qualitative studies have identified that social prescribing may help to reduce service-users' loneliness. Given this, the British Red Cross (a third sector organisation) developed and delivered a national social prescribing service in the United Kingdom to support people who were experiencing, or at risk of, loneliness. Service-users could receive up to 12 weeks of support from a link worker. A mixed methods study was conducted to understand the impact of the support on loneliness, and to identify the facilitators and barriers to service delivery. The study included: (a) analysis of quantitative data collected routinely between May 2017 and December 2019 (n = 10,643) including pre-post analysis of UCLA data (n = 2,250) and matched comparator work to measure changes in loneliness; (b) semi-structured interviews with service-users, link workers and volunteers (n = 60) and (c) a Social Return on Investment Analysis. The majority of the service-users (72.6%, n = 1634/2250) felt less lonely after receiving support. The mean change in UCLA score was -1.84 (95% CI -1.91 to -1.77) of a maximum change of 6.00 (decrease indicates an improvement). Additional benefits included improved wellbeing, increased confidence and life having more purpose. The base case analysis estimated a social return on investment of £3.42 per £1 invested in the service. Having skilled link workers and support tailored to individual needs appeared key. However, challenges included utilising volunteers, meeting some service-users' needs in relation to signposting and sustaining improvements in loneliness. Nonetheless, the service appeared successful in supporting service-users experiencing loneliness.

What is the best approach to adopt for identifying the domains for a new measure of health, social care and carer-related quality of life to measure quality-adjusted life years? Application to the development of the EQ-HWB?

Economic evaluation combines costs and benefits to support decision-making when assessing new interventions using preference-based measures to measure and value benefits in health or health-related quality of life. These health-focused instruments have limited ability to capture wider impacts on informal carers or outcomes in other sectors such as social care. Sector-specific instruments can be used but this is problematic when the impact of an intervention straddles different sectors.An alternative approach is to develop a generic preference-based measure that is sufficiently broad to capture important cross-sector outcomes. We consider the options for the selection of domains for a cross-sector generic measure including how to identify domains, who should provide information on the domains and how this should be framed. Beyond domain identification, considerations of criteria and stakeholder needs are also identified.This paper sets out the case for an approach that relies on the voice of patients, social care users and informal carers as the main source of domains and describes how the approach was operationalised in the 'Extending the QALY' project which developed the new measure, the EQ-HWB (EQ health and wellbeing instrument). We conclude by discussing the strengths and limitations of this approach. The new measure should be sufficiently generic to be used to consistently evaluate health and social care interventions, yet also sensitive enough to pick up important changes in quality of life in patients, social care users and carers.

A Comparison of the SF-6Dv2 and SF-6D UK Utility Values in a Mixed Patient and Healthy Population

BACKGROUND

This paper describes the first evaluation of the construct validity and performance of the newly developed preference-based measure of health, the SF-6D version 2 (SF-6Dv2).

METHOD

Utilising data from the Multi-Instrument Comparison (MIC) project (n = 7932), we explored the descriptive differences in utility values between the SF-6Dv2 and the SF-6D and evaluated the known group validity of both measures by testing the statistical significance of differences in utility values and calculating the effect sizes across known groups. The convergent validity of the SF-6Dv2 was explored by examining whether the SF-6Dv2 is related to alternative validated measures, including the EQ-5D-5L and AQoL-8D.

RESULTS

Differences between the utility values of the SF-6Dv2 and SF-6D were evident; utilities were generally lower for the SF-6Dv2, with larger standard deviations resulting in larger absolute differences between groups. The SF-6Dv2 performed well in terms of known-group validity and successfully distinguished disease severity and between the disease and healthy groups, outperforming the SF-6D in some but not all groups. Convergent validity analyses indicated strong associations between the SF-6Dv2 and the SF-6D, EQ-5D-5L and AQoL-8D utilities.

CONCLUSIONS

The psychometric performance of the SF-6Dv2 is favourable with respect to known group validity and convergent validity, but does not seem to have improved, compared with the SF-6D. However, the new method of valuation has had a substantial impact on the size of absolute differences in utility values, which could impact quality-adjusted life-year results. The economic evaluation of health interventions may therefore be influenced by the choice of the SF-6Dv2 over the SF-6D.

Selecting Bolt-on Dimensions for the EQ-5D: Testing the Impact of Hearing, Sleep, Cognition, Energy, and Relationships on Preferences Using Pairwise Choices

BACKGROUND

Generic preference-based measures (GPBMs) such as the EQ-5D are valid across many conditions, but in some cases, "bolting on" additional dimensions may improve validity. The selection of "bolt-ons" has been based on the psychometric impact of individual dimensions, but preferences provide another important way to select them. This study aims to test the potential of using pairwise choices to inform the selection of bolt-ons for the EQ-5D-5L.

METHODS

General population preferences were collected using an online survey of 1040 UK residents. Three EQ-5D-5L health state pairs were selected based on pairs that had a 50:50 split in respondent preferences from a previous pairwise survey. Participants were presented with pairwise choices of EQ-5D-5L health states without and with bolt-ons of hearing, sleep, cognition, energy, and relationships, each added individually. Logistic models were used to assess the impact of bolt-ons, as well as bolt-ons at different severity levels, on the log odds of responders choosing between health states.

RESULTS

Preferences varied according to the bolt-ons and their severity level (only levels 1, 3, and 5 were used). Additions of bolt-ons at level 1 generally resulted in nonstatistically significant differences while additions of bolt-ons at level 3 and level 5 produced a negative and statistically significant impact on preferences for the health state with the bolt-on. At level 5, hearing had the largest impact, followed by cognition, relationships, energy, and sleep. At level 3, cognition produced the largest impact, followed by hearing and sleep with similar impacts, energy, and relationships. This ordering offers information for bolt-on selection, with hearing and cognition appearing as the most important. The weight placed on the different health problems is not constant across severity levels between bolt-ons.

CONCLUSIONS

Pairwise choices provide a cost-effective approach of generating information on preferences to support bolt-on selection.

Valuing Health State: An EQ-5D-5L Value Set for Ethiopians

OBJECTIVES

There is a growing interest in health technology assessment and economic evaluations in developing countries such as Ethiopia. The objective of this study was to derive an EQ-5D-5L value set from the Ethiopian general population to facilitate cost utility analysis.

METHODS

A nationally representative sample (N = 1050) was recruited using a stratified multistage quota sampling technique. Face-to-face, computer-assisted interviews using the EuroQol Portable Valuation Technology (EQ-PVT) protocol of composite time trade-off (c-TTO) and discrete choice experiments (DCEs) were undertaken to elicit preference scores. The feasibility of the EQ-PVT protocol was pilot tested in a sample of the population (n = 110). A hybrid regression model combining c-TTO and DCE data was used to estimate the final value set.

RESULTS

In the pilot study, the acceptability of the tasks was good, and there were no special concerns with undertaking the c-TTO and DCE tasks. The coefficients generated from a hybrid model were logically consistent. The predicted values for the EQ-5D-5L ranged from -0.718 to 1. Level 5 anxiety/depression had the largest impact on utility decrement (-0.458), whereas level 5 self-care had the least impact (-0.222). The maximum predicted value beyond full health was 0.974 for the 11112 health state.

CONCLUSIONS

This is the first EQ-5D-5L valuation study in Africa using international valuation methods (c-TTO and DCE) and also the first using the EQ-PVT protocol to derive a value set. We expect that the availability of this value set will facilitate health technology assessment and health-related quality-of-life research and inform policy decision making in Ethiopia.

An Updated Systematic Review of Studies Mapping (or Cross-Walking) Measures of Health-Related Quality of Life to Generic Preference-Based Measures to Generate Utility Values

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 methodological developments in this area since a previous review up to 2007.

OBJECTIVE

To provide an updated review of all mapping studies that map from HRQoL measures to target generic preference-based measures (EQ-5D measures, SF-6D, HUI measures, QWB, AQoL measures, 15D/16D/17D, CHU-9D) published from January 2007 to October 2018.

DATA SOURCES

A systematic review of English language articles using a variety of approaches: searching electronic and utilities databases, citation searching, targeted journal and website searches.

STUDY SELECTION

Full papers of studies that mapped from one health measure to a target preference-based measure using formal statistical regression techniques.

DATA EXTRACTION

Undertaken by four authors using predefined data fields including measures, data used, econometric models and assessment of predictive ability.

RESULTS

There were 180 papers with 233 mapping functions in total. Mapping functions were generated to obtain EQ-5D-3L/EQ-5D-5L-EQ-5D-Y (n = 147), SF-6D (n = 45), AQoL-4D/AQoL-8D (n = 12), HUI2/HUI3 (n = 13), 15D (n = 8) CHU-9D (n = 4) and QWB-SA (n = 4) HSUVs. A large number of different regression methods were used with ordinary least squares (OLS) still being the most common approach (used ≥ 75% times within each preference-based measure). The majority of studies assessed the predictive ability of the mapping functions using mean absolute or root mean squared errors (n = 192, 82%), but this was lower when considering errors across different categories of severity (n = 92, 39%) and plots of predictions (n = 120, 52%).

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.

Selecting Bolt-On Dimensions for the EQ-5D: Examining Their Contribution to Health-Related Quality of Life

BACKGROUND

Generic preference-based measures may miss dimensions important for the health-related quality of life (HRQOL) of patients. When this happens, a possible solution is to add bolt-ons. Finch et al. (Finch AP, Brazier JE, Mukuria C, Bjorner JB. An exploratory study on using principal component analysis and confirmatory factor analysis to identify bolt-on dimensions: the EQ-5D case study. Value Health 2017;10:1362-75) have recently shown that bolt-ons can be systematically identified using factor analysis. Nevertheless, because for each bolt-on option a complete re-evaluation may be required, methods to select between them are needed.

OBJECTIVES

To investigate the possibility of selecting bolt-ons using their ability to predict differences in HRQOL. It tests six factors (energy/vitality, satisfaction, relationships, hearing, vision, and speech), and 37 items loading on them, using the EuroQol five-dimensional questionnaire as a case study.

METHODS

Data were obtained from the Multi-Instrument Comparison study, an online survey on health and well-being measures carried out in six countries. Two tests were performed. In the first test, linear regressions were fitted to determine whether different bolt-ons helped explain variations in HRQOL as measured by the Health visual analogue scale. The upper anchor (100) of this scale represents excellent physical, mental, and social health, and the lower anchor (0) represents death. Bolt-on relevance was judged comparing the strength, direction, and statistical significance of unadjusted β coefficients. In the second test, linear regressions were fitted to further investigate whether different factors and items helped explain the negative effect of six chronic conditions on HRQOL. A reduction in the coefficients for the chronic condition dummies meant that the factor or item detected the effect.

RESULTS

Energy/vitality, relationships, and satisfaction reported substantially larger coefficients than did speech, vision, and hearing. Also, items loading on energy/vitality, relationships, and satisfaction generally presented larger coefficients than did those loading on speech, vision, and hearing. The second test did not detect consistent decrements in the coefficients for chronic conditions when testing factors, but it generally did detect consistent decrements when testing items.

CONCLUSIONS

The first test appeared useful for bolt-on selection. Further research is needed before using the second test.

What is the evidence for the performance of generic preference-based measures? A systematic overview of reviews

OBJECTIVE

To assess the evidence on the validity and responsiveness of five commonly used preference-based instruments, the EQ-5D, SF-6D, HUI3, 15D and AQoL, by undertaking a review of reviews.

METHODS

Four databases were investigated using a strategy refined through a highly sensitive filter for systematic reviews. References were screened and a search for grey literature was performed. Identified citations were scrutinized against pre-defined eligibility criteria and data were extracted using a customized extraction template. Evidence on known group validity, convergent validity and responsiveness was extracted and reviewed by narrative synthesis. Quality of the included reviews was assessed using a modified version of the AMSTAR checklist.

RESULTS

Thirty reviews were included, sixteen of which were of excellent or good quality. The body of evidence, covering more than 180 studies, was heavily skewed towards EQ-5D, with significantly fewer studies investigating HUI3 and SF-6D, and very few the 15D and AQoL. There was also lack of head-to-head comparisons between GPBMs and the tests reported by the reviews were often weak. Where there was evidence, EQ-5D, SF-6D, HUI3, 15D and AQoL seemed generally valid and responsive instruments, although not for all conditions. Evidence was not consistently reported across reviews.

CONCLUSIONS

Although generally valid, EQ-5D, SF-6D and HUI3 suffer from some problems and perform inconsistently in some populations. The lack of head-to-head comparisons and the poor reporting impedes the comparative assessment of the performance of GPBMs. This highlights the need for large comparative studies designed to test instruments' performance.

An Exploratory Study on Using Principal-Component Analysis and Confirmatory Factor Analysis to Identify Bolt-On Dimensions: The EQ-5D Case Study

OBJECTIVE

Generic preference-based measures such as the EuroQol five-dimensional questionnaire (EQ-5D) are used in economic evaluation, but may not be appropriate for all conditions. When this happens, a possible solution is adding bolt-ons to expand their descriptive systems. Using review-based methods, studies published to date claimed the relevance of bolt-ons in the presence of poor psychometric results. This approach does not identify the specific dimensions missing from the Generic preference-based measure core descriptive system, and is inappropriate for identifying dimensions that might improve the measure generically. This study explores the use of principal-component analysis (PCA) and confirmatory factor analysis (CFA) for bolt-on identification in the EQ-5D.

METHODS

Data were drawn from the international Multi-Instrument Comparison study, which is an online survey on health and well-being measures in five countries. Analysis was based on a pool of 92 items from nine instruments. Initial content analysis provided a theoretical framework for PCA results interpretation and CFA model development. PCA was used to investigate the underlining dimensional structure and whether EQ-5D items were represented in the identified constructs. CFA was used to confirm the structure. CFA was cross-validated in random halves of the sample.

RESULTS

PCA suggested a nine-component solution, which was confirmed by CFA. This included psychological symptoms, physical functioning, and pain, which were covered by the EQ-5D, and satisfaction, speech/cognition,relationships, hearing, vision, and energy/sleep which were not. These latter factors may represent relevant candidate bolt-ons.

CONCLUSIONS

PCA and CFA appear useful methods for identifying potential bolt-ons dimensions for an instrument such as the EQ-5D.

The Use of Mapping to Estimate Health State Utility Values

Mapping functions are estimated using regression analyses and are frequently used to predict health state utility values (HSUVs) in decision analytic models. Mapping functions are used when evidence on the required preference-based measure (PBM) is not available, or where modelled values are required for a decision analytic model, for example to control for important sociodemographic variables (such as age or gender). This article provides an overview of the latest recommendations including pre-mapping considerations, the mapping process including data requirements for undertaking the estimation of mapping functions, regression models for estimating mapping functions, assessing performance and reporting standards for mapping studies. Examples in rheumatoid arthritis are used for illustration. When reporting the results of mapping standards the following should be reported: a description of the dataset used (including distributions of variables used) and any analysis used to inform the selection of the model type and model specification. The regression method and specification should be justified, and as summary statistics may mask systematic bias in errors, plots comparing observed and predicted HSUVs. The final model (coefficients, error term(s), variance and covariance) should be reported together with a worked example. It is important to ensure that good practice is followed as any mapping functions will only be as appropriate and accurate as the method used to obtain them; for example, mapping should not be used if there is no overlap between the explanatory and target variables.

Predicting Productivity Losses from Health-Related Quality of Life Using Patient Data

OBJECTIVE

This paper estimates productivity loss using the health of the patient in order to allow indirect estimation of these costs for inclusion in economic evaluation.

METHODS

Data from two surveys of inpatients [Health outcomes data repository (HODaR) sample (n = 42,442) and health improvement and patient outcomes (HIPO) sample (n = 6046)] were used. The number of days off paid employment or normal activities (excluding paid employment) was modelled using the health of the patients measured by the EQ-5D, international classification of diseases (ICD) chapters, and other health and sociodemographic data. Two-part models (TPMs) and zero-inflated negative binomial (ZINB) models were identified as the most appropriate specifications, given large spikes at the minimum and maximum days for the dependent variable. Analysis was undertaken separately for the two datasets to account for differences in recall period and identification of those who were employed.

RESULTS

Models were able to reflect the large spike at the minimum (zero days) but not the maximum, with TPMs doing slightly better than the ZINB model. The EQ-5D was negatively associated with days off employment and normal activities in both datasets, but ICD chapters only had statistically significant coefficients for some chapters in the HODaR.

CONCLUSIONS

TPMs can be used to predict productivity loss associated with the health of the patient to inform economic evaluation. Limitations include recall and response bias and identification of who is employed in the HODaR, while the HIPO suffers from a small sample size. Both samples exclude some patient groups.

A pragmatic randomised controlled trial assessing the non-inferiority of counselling for depression versus cognitive-behaviour therapy for patients in primary care meeting a diagnosis of moderate or severe depression (PRaCTICED): Study protocol for a randomised controlled trial

BACKGROUND

NICE guidelines state cognitive behavioural therapy (CBT) is a front-line psychological treatment for people presenting with depression in primary care. Counselling for Depression (CfD), a form of Person-Centred Experiential therapy, is also offered within Improving Access to Psychological Therapies (IAPT) services for moderate depression but its effectiveness for severe depression has not been investigated. A full-scale randomised controlled trial to determine the efficacy and cost-effectiveness of CfD is required.

METHODS

PRaCTICED is a two-arm, parallel group, non-inferiority randomised controlled trial comparing CfD against CBT. It is embedded within the local IAPT service using a stepped care service delivery model where CBT and CfD are routinely offered at step 3. Trial inclusion criteria comprise patients aged 18 years or over, wishing to work on their depression, judged to require a step 3 intervention, and meeting an ICD-10 diagnosis of moderate or severe depression. Patients are randomised using a centralised, web-based system to CfD or CBT with each treatment being delivered up to a maximum 20 sessions. Both interventions are manualised with treatment fidelity tested via supervision and random sampling of sessions using adherence/competency scales. The primary outcome measure is the Patient Health Questionnaire-9 collected at baseline, 6 and 12 months. Secondary outcome measures tap depression, generic psychological distress, anxiety, functioning and quality of life. Cost-effectiveness is determined by a patient service receipt questionnaire. Exit interviews are conducted with patients by research assessors blind to treatment allocation. The trial requires 500 patients (250 per arm) to test the non-inferiority hypothesis of -2 PHQ-9 points at the one-sided, 2.5% significance level with 90% power, assuming no underlying difference and a standard deviation of 6.9. The primary analysis will be undertaken on all patients randomised (intent to treat) alongside per-protocol and complier-average causal effect analyses as recommended by the extension to the CONSORT statement for non-inferiority trials.

DISCUSSION

This large-scale trial utilises routinely collected outcome data as well as specific trial data to provide evidence of the comparative efficacy and cost-effectiveness of Counselling for Depression compared with Cognitive Behaviour Therapy as delivered within the UK government's Improving Access to Psychological Therapies initiative.

TRIAL REGISTRATION

Controlled Trials ISRCTN Registry, ISRCTN06461651 . Registered on 14 September 2014.

Olaparib for Maintenance Treatment of BRCA 1 or 2 Mutated, Relapsed, Platinum-Sensitive Ovarian, Fallopian Tube and Peritoneal Cancer in People Whose Relapsed Disease has Responded to Platinum-Based Chemotherapy: An Evidence Review Group Perspective of a NICE Single Technology Appraisal

As part of its Single Technology Appraisal process, the National Institute for Health and Care Excellence (NICE) invited the manufacturer of olaparib (AstraZeneca) to submit evidence on the clinical and cost effectiveness of olaparib for the maintenance treatment of BRCA1/2 mutated (BRCAm), platinum-sensitive relapsed (PSR) ovarian, fallopian tube and peritoneal cancer in people whose relapsed disease has responded to platinum-based chemotherapy. The Evidence Review Group (ERG) produced a critical review of the evidence contained within the company's submission (CS) to NICE. The clinical evidence related to one phase II, double-blind randomised controlled trial that recruited 265 patients with PSR serous ovarian cancer (OC) regardless of BRCAm status. Patients received olaparib 400 mg twice daily (b.i.d.) or matched placebo. In the whole population, the primary endpoint of progression-free survival (PFS) was met (hazard ratio [HR] 0.35; 95 % confidence interval [CI] 0.25-0.49, p < 0.01) for olaparib versus placebo. The BRCAm subgroup analysis (added after the study commenced but 1 month before the primary analysis was undertaken) reported an HR for PFS of 0.18 (95 % CI 0.10-0.31, p < 0.0001) for olaparib versus placebo, though interaction tests appeared inconclusive. Overall survival was not statistically significant in the whole group (HR 0.88; 95 % CI 0.64-1.21; p = 0.44) or the BRCAm subgroup (0.73; 95 % CI 0.45-1.17; p = 0.19), though treatment switching may have confounded results. The exclusion of data from sites allowing crossover resulted in an HR for overall survival (OS) of 0.52 (95 % CI 0.28-0.97, p = 0.039) in the BRCAm group. Health-related quality-of-life measures were not significantly different between groups. All post hoc exploratory outcomes (time to treatment discontinuation/death, time to first subsequent therapy/death, and time to second subsequent therapy/death) were statistically significantly better in the olaparib arm in the whole population and the BRCAm subgroup analyses. Adverse events were more frequent for olaparib but were largely minor or manageable. The company's semi-Markov model assessed the cost effectiveness of olaparib versus routine surveillance in patients with BRCAm PSR OC from a National Health Service (NHS) and Personal Social Services (PSS) perspective over a lifetime horizon. The model suggests that the incremental cost-effectiveness ratio (ICER) for olaparib versus routine surveillance is expected to be approximately £49,146 per quality-adjusted life-year (QALY) gained. The ERG did not consider the company's cost-effectiveness estimates to be credible. Additional ERG analyses suggested that the ICER is likely to be more than £92,214 per QALY gained. Additional analyses provided by the company in patients who received three or more lines of chemotherapy suggested a more favourable cost-effectiveness profile for olaparib. The NICE Appraisal Committee recommended olaparib for this subgroup provided the cost of olaparib for people who continue to receive treatment after 15 months will be met by the company.

Sensitivity and responsiveness of the EQ-5D-3L in patients with uncontrolled focal seizures: an analysis of Phase III trials of adjunctive brivaracetam

Purpose

Preference-based measures are required to measure the impact of interventions for cost-effectiveness analysis. This study assessed the psychometric performance of the EQ-5D-3L in adults with uncontrolled focal (partial-onset) seizures.

Methods

Data from three Phase III studies of an antiepileptic drug (adjunctive brivaracetam; n = 1095) were used. Analysis included correlations between EQ-5D-3L and Quality of Life in Epilepsy Inventory (QOLIE-31P) and seizure frequency. Known group validity was based on ability of the EQ-5D-3L to discriminate between baseline QOLIE-31P total scores, seizure type and number of antiepileptic drugs using effect sizes (ES). Responsiveness assessed proportions reporting highest or lowest scores, overall change using standardized response means (SRM) and change by responder and clinician/patient evaluation groups using ES.

Results

Correlations were weak to moderate (ρ = 0.2–0.4) between EQ-5D-3L dimensions and QOLIE-31P subscales, apart from medication effects (ρ < 0.1); seizure frequency was not associated with either measure. Known group analysis had small ES. A quarter (24.9%) of patients had a baseline EQ-5D-3L utility score of 1 (full health) but lower average QOLIE-31P scores. SRMs were small (<0.1) in EQ-5D-3L compared with 0.1–0.4 for QOLIE-31P subscales. Results across the studies were mixed for responder status and clinician/patient evaluation of improvement for EQ-5D-3L.

Conclusions

EQ-5D-3L had weak-to-moderate correlations with QOLIE-31P and varied with QOLIE-31P severity groups, but showed less responsiveness than QOLIE-31P. Given this lack of sensitivity, EQ-5D-3L may not be appropriate for measuring the impact of interventions in cost-effectiveness analysis in this population and disease-specific preference-based measures may be more appropriate.

Electronic supplementary material

The online version of this article (doi:10.1007/s11136-016-1483-3) contains supplementary material, which is available to authorized users.

Estimating informal care inputs associated with EQ-5D for use in economic evaluation

OBJECTIVES

This paper estimates informal care need using the health of the patient. The results can be used to predict changes in informal care associated with changes in the health of the patient measured using EQ-5D.

METHODS

Data was used from a prospective survey of inpatients containing 59,512 complete responses across 44,494 individuals. The number of days a friend or relative has needed to provide care or help with normal activities in the last 6 weeks was estimated using the health of the patient measured by EQ-5D, ICD chapter and other health and sociodemographic data. A variety of different regression models were estimated that are appropriate for the distribution of the informal care dependent variable, which has large spikes at 0 (zero informal care) and 42 days (informal care every day).

RESULTS

The preferred model that most accurately predicts the distribution of the data is the zero-inflated negative binomial with variable inflation. The results indicate that improving the health of the patient reduces informal care need. The relationship between ICD chapter and informal care need is not as clear.

CONCLUSIONS

The preferred zero-inflated negative binomial with variable inflation model can be used to predict changes in informal care associated with changes in the health of the patient measured using EQ-5D and these results can be applied to existing datasets to inform economic evaluation. Limitations include recall bias and response bias of the informal care data, and restrictions of the dataset to exclude some patient groups.

Comparison of Modes of Administration and Alternative Formats for Eliciting Societal Preferences for Burden of Illness

BACKGROUND

Proposals for value-based assessment, made by the National Institute of Health and Care Excellence (NICE) in the UK, recommended that burden of illness (BOI) should be used to weight QALY gain. This paper explores some of the methodological issues in eliciting societal preferences for BOI.

AIMS

This study explores the impact of mode of administration and framing in a survey for eliciting societal preferences for BOI.

METHODS

A pairwise comparison survey with six arms was conducted online and via face-to-face interviews, involving two different wordings of questions and the inclusion/exclusion of pictures. Respondents were asked which of two patient groups they thought a publically funded health service should treat, where the groups varied by life expectancy without treatment, health-related quality of life (HRQOL) without treatment, survival gain from treatment, and HRQOL gain from treatment. Responses across different modes of administration, wording and use of pictures were compared using chi-squared tests and probit regression analysis controlling for respondent socio-demographic characteristics.

RESULTS

The sample contained 371 respondents: 69 were interviewed and 302 completed the questionnaire online. There were some differences in socio-demographic characteristics across the online and interview samples. Online respondents were less likely to choose the group with higher BOI and more likely to treat those with a higher QALY gain, but there were no statistically significant differences by wording or the inclusion of pictures for the majority of questions. Regression analysis confirmed these results. Respondents chose to treat the group with larger treatment gain, but there was little support for treating the group with higher BOI. Respondents also preferred to treat the group with treatment gains in life expectancy rather than HRQOL.

CONCLUSIONS

Mode of administration did impact on responses, whereas question wording and pictures did not impact on responses, even after controlling for the socio-demographic characteristics of respondents in the regression analysis.

Eliciting Societal Preferences for Weighting QALYs for Burden of Illness and End of Life

OBJECTIVES

. Recent proposals for value-based assessment, made by the National Institute of Health and Care Excellence (NICE) in the United Kingdom, recommended that burden of illness (BOI) should replace end of life (EOL) as a factor for consideration when deciding on new health technologies. This article reports on a study eliciting societal preferences for 1) BOI from a medical condition, defined as quality-adjusted life year (QALY) loss due to premature mortality and prospective morbidity, and 2) EOL, defined as expected life expectancy of less than 2 years and expected life expectancy gain from new treatment of 3 months or more.

METHODS

. A discrete choice experiment survey was conducted with an online UK general population sample. Respondents chose whether they thought the health service should treat patient group A or B: life expectancy and health-related quality of life (HRQOL) with current treatment or life expectancy and HRQOL gains from new treatment, respectively. These attributes were used to derive BOI, QALY gain, and EOL. The respondents' choices were analyzed using conditional logistic regression with a range of specifications examined, including BOI or EOL, QALY gain and QALY gain squared, and robustness. QALY weights were estimated.

RESULTS

. The sample of 3669 respondents was representative of the UK population for age and sex. QALY gain had a positive and significant coefficient across all models. QALY gain squared term was negative and significant across all models, indicating a diminishing marginal social value from QALY gains. When included, the BOI coefficient was generally small, positive, and significant, but this was not consistent across the different life expectancy variants. EOL was always positive and significant.

CONCLUSIONS

. The social value of a QALY gain is not equal between recipients but depends on whether they are end of life, and it may depend on the prospective burden of illness.

Deriving a Preference-Based Measure for Myelofibrosis from the EORTC QLQ-C30 and the MF-SAF

BACKGROUND

Utility values are required for economic evaluation using cost-utility analyses. Often, generic measures such as the EuroQol five-dimensional questionnaire are used, but this may not appropriately reflect the health-related quality of life of patients with cancer including myelofibrosis.

OBJECTIVE

To derive a condition-specific preference-based measure for myelofibrosis using appropriate existing measures, the Myelofibrosis-Symptom Assessment Form and the European Organisation for Research and Treatment of Cancer Quality of Life 30 Questionnaire.

METHODS

Data from the Controlled Myelofibrosis Study with Oral JAK Inhibitor Treatment trial (n = 309) were used to derive the health state classification system. Psychometric and factor analyses were used to determine the dimensions of the classification system. Psychometric and Rasch analyses were then used to select an item to represent each dimension. Item selection was validated with experts. A selection of health states was valued by members of the general population using time trade-off. Finally, health state values were modeled using regression analysis to produce utility values for every state.

RESULTS

The Myelofibrosis 8 dimensions has eight dimensions: physical functioning, emotional functioning, fatigue, itchiness, pain under ribs on the left side, abdominal discomfort, bone or muscle pain, and night sweats. Regression models were estimated using time trade-off data from 246 members of the general population valuing a total of 33 states. The best performing model was a random effects maximum likelihood model producing utility values ranging from 0.089 to 1.

CONCLUSIONS

The Myelofibrosis 8 dimensions is a condition-specific preference-based measure for myelofibrosis. This measure can be used to generate utility values for myelofibrosis for any data set containing the Myelofibrosis-Symptom Assessment Form and the European Organisation for Research and Treatment of Cancer Quality of Life 30 Questionnaire data.

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 systematic review, psychometric analysis and qualitative assessment of generic preference-based measures of health in mental health populations and the estimation of mapping functions from widely used specific measures

BACKGROUND

Generic preference-based measures of health like the EQ-5D and SF-6D(®) are increasingly being used in economic evaluation and outcome assessment. However, there are concerns as to whether or not these generic measures are appropriate for use in people with mental health problems.

OBJECTIVES

The EQ-5D and SF-36(®) (including its derivatives the SF-12(®) and SF-6D) were assessed using the psychometric criteria of validity and responsiveness using quantitative and qualitative methods. Another aim was to estimate mapping functions between the EQ-5D and SF-6D and condition-specific measures, where appropriate.

DESIGN

Four studies were undertaken to examine the appropriateness of the measures: (1) a systematic review of quantitative evidence on validity and responsiveness; (2) a further quantitative assessment of these criteria using existing data sets; (3) a review of qualitative research on the quality of life of people with mental health problems; and (4) qualitative semistructured interviews of people with a full range of problems. A fifth study estimated mapping functions between mental health-specific measures and the EQ-5D and SF-6D.

SETTING

A choice of venue was offered for the interviews including the participant's own home, a room at the university or a centre frequently used by mental health services.

PARTICIPANTS

The interviews were undertaken with 19 people with a broad range of mental health problems at varying levels of severity.

MAIN OUTCOME MEASURES

The reviews included the EQ-5D and SF-36 (and the SF-12 and SF-6D). The psychometric analysis included the Hospital Anxiety and Depression Scale (HADS), Clinical Outcomes in Routine Evaluation - Outcome Measure (CORE-OM), Generalised Anxiety Disorder Assessment (GAD-7), General Health Questionnaire (GHQ-12) and Patient Health Questionnaire (PHQ-9).

RESULTS

(1) and (2) The EQ-5D and SF-36 achieved an adequate level of performance in depression, and to some extent in anxiety and personality disorder. Results from the psychometric analyses in schizophrenia and bipolar disorder have been more mixed. (3) A framework analysis of 13 studies identified six major themes. (4) The interview data fitted the themes from the review well and resulted in minor modifications to the themes. The final set of themes comprised: well-being and ill-being; control, autonomy and choice; self-perception; belonging; activity; hope and hopelessness; and physical health.

CONCLUSIONS

The EQ-5D and SF-36 achieved mixed results in the quantitative testing against psychometric criteria. The qualitative analysis suggests this is because they provide a very limited coverage of themes identified by people with mental health problems. Recommendations for future work include the development of new preference-based measures in mental health that are based on, or substantially revise, an existing measure.

FUNDING

The Medical Research Council.