The document discusses the EQ-5D, a standardized instrument used to measure health outcomes. It describes the EQ-5D as comprising a descriptive system covering 5 dimensions of health and a visual analog scale (EQ-VAS). Country-specific value sets allow EQ-5D health states to be converted into a single summary index number representing health-related quality of life. The EQ-5D is widely used internationally in cost-effectiveness analysis and other areas to inform healthcare decisions. Challenges in analyzing EQ-5D data and future directions for outcomes measurement are also addressed.

1. What is the EQ-5D?
The use of the EQ-5D internationally
Professor Nancy J Devlin
Office of Health Economics
Workshop on Measuring Patient Reported Outcomes
Stockholm • 31 May 2013

2. 1. The EuroQol Group
2. Introduction to the EQ-5D instrument
3. Values and valuation methods
4. Uses of EQ-5D
5. Analysing EQ-5D data: profiles, index-weighted
profiles and EQ-VAS
6. Some analytical challenges
7. The future of outcomes measurement
Content

3. 1. The EuroQol Group

4. • Established > 25 years ago
• Not-for-profit organisation
• > 75 researchers internationally; office in Rotterdam
• The EuroQol Group aims to improve decisions about
health and health care throughout the world by
developing, promoting and supporting the use of
instruments with the widest possible applicability for
the measurement and valuation of health.

5. • To provide leadership in the research and development of instruments that
describe and value health
• To promote the use of instruments developed by the EuroQol Group and to
support individuals and organization across the world seeking to use those
instruments
• To foster and support an international community of researchers whose
activity informs the development and application of EuroQol Group
instruments
• To ensure access to the accumulated research expertise of the EuroQol
Group and to actively promote the transfer of knowledge and evidence
regarding the use, analysis, and interpretation of measures developed by
the EuroQol Group
• To support promising early career researchers in the field of health and
quality of life research through involvement in EuroQol Group activities.
Mission

6. 2. Introduction to the EQ-5D

7. • EQ-5D comprises two distinct self-report elements,
providing three principal approaches to analysis
(1) the EQ-5D profile: the patients’ self reported health on
the dimensions/levels of the descriptive system
(2) the EQ-VAS: the patients’ own global rating of their
overall health, on a scale from 0 (worst possible health) to
100 (best possible health)
• Both types of data can be the focus of analysis, plus
(3) Profiles can be summarised using ‘value sets’ (EQ-5D
Index) which reflect the preferences of the general public.

8. EQ-5D ‘profile’

9. • EQ-VAS

10. EQ-5D-5L

11. 3. Values and valuation methods

12. Index weighting EQ-5D profiles
This weight depends on:
- who is asked
- which stated
preference method is
used
- how the preference
data are modelled eg the
MVH ‘N3’ term

13. EQ-5D ‘value sets’
• Value sets for the 3L are
available for a range of
countries.
• An interim value set is available
for the 5L (from a cross-over
study).
• Value set studies for the 5L are
underway or planned in > 10
countries.

14. • Whose values count?
• Generally argued that, for resource allocation decisions,
it is the preferences of the general public that are
relevant.
• What type of methods are used?
• The values represent the views (‘preferences’) of people
about how good or bad health states are.
• Using ‘stated preferences’ methods
• The methods available include Time Trade Off (TTO),
Discrete Choice Experiments (DCE); Visual Analogue
Scale (VAS) and Standard Gamble (SG).
Methods for valuing EQ-5D

15. TTO
• The method works by finding how much time people would be
willing to trade off.
• Choice between:
 x years of full health (‘Life A’), followed by death, and
 t years in the health state to be valued (‘Life B’), followed
by death
 Where x < t
 X is varied until Life A and Life B are considered equally
good/bad.
• Aim is to find the exact x that makes the person indifferent
between Life A and Life B.
• The worse the health state, the more time we expect to be
traded (and the lower the value).

17. DCE

18. 4. Uses of EQ-5D

19. 1. HTA
• EQ-5D recommended by NICE (clinical trials; obs. studies)
• Informs decisions about reimbursement and (from 2014)
pricing of new technologies (VBP)
2. Population health surveys
• E.g. in the NHS, informs ‘needs based’ allocations of
budgets across regions
3. Routine use in the health care system
• NHS PROMs programme
• Multiple uses of these data e.g. assessing provider
performance; value for money of services.
Principal applications of EQ-5D

20. • EQ-5D widely used in clinical trials and observational
studies around the world to inform HTA
• Widely accepted by HTA agencies; use is expanding (e.g.
as HTA systems evolve in emerging markets)
• And in public health population surveys
• its brevity is a big advantage
• Routine use in health care systems relatively new
• English NHS leading the way
• Interest and use elsewhere including: Canada, New
Zealand, Sweden, China…
International use

21. 5. Analysing EQ-5D data

22. • EQ-5D profiles
• Simple descriptive stats (number and % problems)
• Categories of change
• Analyses by dimension.
• Index-weighted profiles
• Required in estimation of QALYs
• Caution required in other applications.
• Analysis of EQ-VAS data

23. • In cost effectiveness analysis, patients’ profiles assigned QoL
‘weights’: EQ Index
• Reflect preferences (‘utilities’) of the general public
obtained using stated preference methods
• Normative judgement – allocation of taxpayer resources
• Do the same arguments apply to PROMs?
• There is no ‘neutral’ way to summarise profiles.
• Each value set will have its own properties.
• Can bias statistical inference.
Parkin, D., Rice, N. and Devlin, N. (2010) Statistical analysis of EQ-5D profiles: does the use of value sets bias
inference? Medical Decision Making (forthcoming).
Index weighted profiles

24. Profiles – simple distributions
Feng, Y., Parkin, D. and Devlin, N.J. (2012) Assessing the performance of the EQ-VAS in the NHS PROMs
Programme. Research Paper 12/01. London: Office of Health Economics.

25. • Paretian classification of health change
• Comparing two EQ-5D, differences between them may
be:
- Mixed (better on some dimensions, worse on others)
- Better (better on at least one dimension, no worse on others)
- Worse (worse on at least one dimension, no better on others)
- Exactly the same
• % in each category
Profiles – categorising change
Feng, Y., Parkin, D. and Devlin, N.J. (2012) Assessing the performance of the EQ-VAS in the NHS PROMs Programme.
Research Paper 12/01. London: Office of Health Economics.

26. Hospital performance by profile dimension
Usual activities
Pain/discomfort
Gutacker, N., Bojke, C., Daidone, S., Devlin, N. and Street, A. (2012) Analysing hospital variations in health outcome at the level
of EQ-5D dimensions. Research Paper No. 74, Centre for Health Economics, University of York.

27. EQ-VAS and EQ Index distributions
Feng, Y., Parkin, D. and Devlin, N.J. (2012) Assessing the performance of the EQ-VAS in the NHS PROMs Programme.
Research Paper 12/01. London: Office of Health Economics.

28. 6. Some analytical challenges

29. 1. Adequacy of EQ-5D as a measurement instrument
• ‘Missing’ dimensions?
• Exploration of ‘bolt on’ dimensions
2. EQ-5D and EQ-VAS measure conceptually different
things
3. Differences between description (patients’ self-
reported profiles) and valuation (general public
valuation of those same states)
• Potential issue for the 5L?

30. 4. Other challenges with use of EQ-Index
• Value sets potentially bias statistical inference
• Bi-modality (‘clustering’) in distributions of EQ Index
data
5. Statistical significance vs. m.i.d
6. Challenges specific to use of EQ-5D in PROMs
• E.g. the importance of case mix adjustment

31. 7. The future of outcomes
measurement

32. 1. The future of health technology appraisal
• HTA as a continuous process?
• Evaluation of technologies ‘in the real world, in real time’,
rather than a ‘one-off’ reimbursement decision
• Not just ‘new’ technologies, but all health care services
• ‘Real world data’ – e.g. use of EQ-5D in routine monitoring
and evaluation of outcomes and quality of care
• Part of the increasing availability and importance of ‘big
data’ generally.
Speculations about the future…

33. 2. Continued importance of patients’ subjective views of
their own health
• “The use of PRO instruments is part of a general
movement toward the idea that the patient, properly
queried, is the best source of information about how he
or she feels”. [FDA 2006]
• Growing recognition of the importance of the patients’
perspective (e.g. on their own health – and maybe on
health state values?)
Speculations about the future…

34. 3. The health and social care interface
• Measurement of outcomes beyond health care
• Can measures of HR-QoL capture wider sorts of
outcomes?
• Important to understand how measures of HR-QoL—such
as—EQ-5D correspond to measures of subjective
wellbeing (e.g. ‘happiness’)
Speculations about the future…

35. To enquire about additional information and analyses, please contact
Prof Nancy Devlin at ndevlin@ohe.org
To keep up with the latest news and research, subscribe to our blog, OHE News.
Follow us on Twitter @OHENews, LinkedIn and SlideShare.
Office of Health Economics (OHE)
Southside, 7th Floor
105 Victoria Street
London SW1E 6QT
United Kingdom
+44 20 7747 8850
www.ohe.org
OHE’s publications may be downloaded free of charge for registered users of its website.
©2013 OHE