Competing Views on the English EQ-5D-5L Valuation Set

Enabling QALY estimation in mental health trials and care settings: mapping from the PHQ-9 and GAD-7 to the ReQoL-UI or EQ-5D-5L using mixture models

PURPOSE

Patient-reported outcome measures (PROMs) are commonly collected in trials and some care settings, but preference-based PROMs required for economic evaluation are often missing. For these situations, mapping models are needed to predict preference-based (aka utility) scores. Our objective is to develop a series of mapping models to predict preference-based scores from two mental health PROMs: Patient Health Questionnaire-9 (PHQ-9; depression) and Generalised Anxiety Questionnaire-7 (GAD-7; anxiety). We focus on preference-based scores for the more physical-health-focussed EQ-5D (five-level England and US value set, and three-level UK cross-walk) and more mental-health-focussed Recovering Quality-of-Life Utility Index (ReQoL-UI).

METHODS

We used trial data from the Improving Access to Psychological Therapies (IAPT) mental health services (now called NHS Talking Therapies), England, with a focus on people with depression and/or anxiety caseness. We estimated adjusted limited dependent variable or beta mixture models (ALDVMMs or Betamix, respectively) using GAD-7, PHQ-9, age, and sex as covariates. We followed ISPOR mapping guidance, including assessing model fit using statistical and graphical techniques.

RESULTS

Over six data collection time-points between baseline and 12-months, 1340 observed values (N ≤ 353) were available for analysis. The best fitting ALDVMMs had 4-components with covariates of PHQ-9, GAD-7, sex, and age; age was not a probability variable for the final ReQoL-UI mapping model. Betamix had practical benefits over ALDVMMs only when mapping to the US value set.

CONCLUSION

Our mapping functions can predict EQ-5D-5L or ReQoL-UI related utility scores for QALY estimation as a function of variables routinely collected within mental health services or trials, such as the PHQ-9 and/or GAD-7.

Estimating Cost-Effectiveness Using Alternative Preference-Based Scores and Within-Trial Methods: Exploring the Dynamics of the Quality-Adjusted Life-Year Using the EQ-5D 5-Level Version and Recovering Quality of Life Utility Index

OBJECTIVES

This study aimed to explore quality-adjusted life-year (QALY) and subsequent cost-effectiveness estimates based on the more physical health-focused EQ-5D 5-level version (EQ-5D-5L) value set for England or cross-walked EQ-5D 3-level version UK value set scores or more mental health recovery-focused Recovering Quality of Life Utility Index (ReQoL-UI), when using alternative within-trial statistical methods. We describe possible reasons for the different QALY estimates based on the interaction between item scores, health state profiles, preference-based scores, and mathematical and statistical methods chosen.

METHODS

QALYs are calculated over 8 weeks from a case study 2:1 (intervention:control) randomized controlled trial in patients with anxiety or depression. Complete case and with missing cases imputed using multiple-imputation analyses are conducted, using unadjusted and regression baseline-adjusted QALYs. Cost-effectiveness is judged using incremental cost-effectiveness ratios and acceptability curves. We use previously established psychometric results to reflect on estimated QALYs.

RESULTS

A total of 361 people (241:120) were randomized. EQ-5D-5L crosswalk produced higher incremental QALYs than the value set for England or ReQoL-UI, which produced similar unadjusted QALYs, but contrasting baseline-adjusted QALYs. Probability of cost-effectiveness <£30 000 per QALY ranged from 6% (complete case ReQoL-UI baseline-adjusted QALYs) to 64.3% (multiple-imputation EQ-5D-5L crosswalk unadjusted QALYs). The control arm improved more on average than the intervention arm on the ReQoL-UI, a result not mirrored on the EQ-5D-5L nor condition-specific (Patient-Health Questionnaire-9, depression; Generalized Anxiety Disorder-7, anxiety) measures.

CONCLUSIONS

ReQoL-UI produced contradictory cost-effectiveness results relative to the EQ-5D-5L. The EQ-5D-5L's better responsiveness and "anxiety/depression" and "usual activities" items drove the incremental QALY results. The ReQoL-UI's single physical health item and "personal recovery" construct may have influenced its lower 8-week incremental QALY estimates in this patient sample.

Psychometric assessment of EQ-5D-5L and ReQoL measures in patients with anxiety and depression: construct validity and responsiveness

PURPOSE

Generic health measures have been questioned for quantifying mental-health-related outcomes. In patients with anxiety and/or depression, our aim is to assess the psychometric properties of the preference-based EQ-5D-5L (generic health) and ReQoL-UI (recovery-focussed quality of life) for economic evaluation against the PHQ-9 (depression) and GAD-7 (anxiety). EQ-5D-5L anxiety/depression item and ReQoL-10 are also assessed.

METHODS

A 2:1 (intervention: control) randomised controlled trial collected measures at baseline and 8 weeks post baseline; in the intervention arm, data were also collected 3, 6, 9, and 12-months post baseline. EQ-5D-5L preference-based scores were obtained from the value set for England (VSE) and 'cross-walked' EQ-5D-3L United Kingdom (UK) value set scores. ReQoL-UI preference-based scores were obtained from its UK value set as applied to seven ReQoL-10 items. EQ-5D-5L and ReQoL measures' construct validity and responsiveness were assessed compared against PHQ-9 and GAD-7 scores and group cut-offs.

RESULTS

361 people were randomised to intervention (241) or control (120). ReQoL-UI/-10 had better construct validity with depression severity than the EQ-5D-5L (VSE/cross-walk scores), which had relatively better construct validity with anxiety severity than the ReQoL-UI/-10. Across all intervention-arm time-points relative to baseline, responsiveness was generally better for EQ-5D-5L (VSE in particular) than ReQoL-UI, but worse than ReQoL-10.

CONCLUSION

There is insufficient evidence to recommend the ReQoL-UI over EQ-5D-5L for economic evaluations to capture anxiety severity. However, there may be rationale for recommending the ReQoL-UI over the EQ-5D-5L to capture depression severity given its better construct validity, albeit poorer responsiveness, and if recovery-focussed quality of life relative to condition-specific symptomology is the construct of interest.

Inter-rater reliability of care home staff's proxy judgements with residents' assessments of their own health-related quality of life: an analysis of the PATCH trial EQ-5D data

OBJECTIVES

to compare care staff proxies with care home residents' self-assessment of their health-related quality of life (HRQoL).

METHODS

we assessed the degree of inter-rater reliability between residents and care staff proxies for the EQ-5D-5L index, domains and EQ Visual Analogue Scale at baseline, 3 months and 6 months, collected as part of the PATCH trial. We calculated kappa scores. Interpreted as <0 no agreement, 0-0.2 slight, 0.21-0.60 fair to moderate and >0.6 substantial to almost perfect agreement. Qualitative interviews with care staff and researchers explored the challenges of completing these questions.

RESULTS

over 50% of the HRQoL data from residents was missing at baseline compared with a 100% completion rate by care staff proxies. A fair-to-moderate level of agreement was found for the EQ-5D-5L index. A higher level of agreement was achieved for the EQ-5D-5L domains of mobility and pain. Resident 'non-completers' were more likely to: be older, have stayed a longer duration in the care home, have lower Barthel Index and Physical Activity and Mobility in Residential Care (PAM-RC) scores, a greater number of co-morbidities and have joined the trial through consultee agreement. Interviews with staff and researchers indicated that it was easier to rate residents' mobility levels than other domains, but in general it was difficult to obtain data from residents or to make an accurate proxy judgement for those with dementia.

CONCLUSIONS

whilst assessing HRQoL by care staff proxy completion provides a more complete dataset, uncertainty remains as to how representative these values are for different groups of residents within care homes.