1. The Application of EQ-5D in
Reimbursement Decision Making
– The Case of NICE
Professor Nancy J. Devlin
Director of Research, Office of Health Economics
Chair, EuroQol Group Executive Committee
Valuing Health Outcomes for Healthcare Decision Making Using the
EQ-5D: A Symposium for Policy Makers and Researchers in Asia
Singapore
22 March 2013

2. Contents
1. Introduction: HTA, value for money, and outcomes
measurement
2. NICE Health Technology Appraisal: purpose & process
3. The relationship between cost effectiveness and NICE
decisions
4. The role of EQ-5D in NICE decision making
5. Collecting and analysing EQ-5D data to inform HTA
6. Utility weighting EQ-5D profiles – the role of value sets
7. What if EQ-5D data are not available?
8. Is EQ-5D always appropriate?
9. Concluding remarks

3. 1. HTA, value for money, and
outcomes measurement
• Health care budgets are limited
• Increasing demand for health care, e.g. from
– Aging populations
– Rising expectations about health and health care
– Improvements in health care technology
• Most new technologies improve health (length/quality of
life), but also increase costs
• Reimbursing such technologies has an opportunity cost:
the benefits possible from the next best opportunity
foregone

4. • How can we judge whether new technologies are good
value for money?
• Need to be able to compare health gained with health
foregone
• Aim is to achieve an allocation of budgets that
maximises health
• Cost effectiveness analysis provides a means of
comparing value for money
• This requires a standardised measure of health
outcomes, e.g. the QALY
• Which in turn requires a standardised, generic means of
measuring patients‘ quality of life, e.g. EQ-5D

5. 2. NICE HTA: Purpose & Process
• Purpose: ―to offer health professionals guidance on the use of
technologies, based on a rigorous review of the available
evidence‖
• In doing so, it takes the following six matters into account:
– The clinical needs of patients
– NHS priorities
– The broad balance between benefits and
costs, incorporating both clinical and cost effectiveness
– Potential impact on other NHS resources
– Encouragement of innovation
– Guidance from ministers on the resources available

6. 3. Cost Effectiveness and NICE Decisions
– ―[NICE] should, generally, accept as cost effective those
interventions with an incremental cost-effectiveness
ratio of less than £20,000 per QALY and that there
should be increasingly strong reasons for accepting as
cost effective interventions with an incremental cost-
effectiveness ratio of over £30,000 per QALY.‖

7. Modelling NICE Decisions
At average levels for all covariates, a decision would have a
50% chance of rejection if its ICER were £45,118/QALY
Source: Dakin, Devlin, Rice, Parkin, O’Neill, Feng (2013) The influence of cost effectiveness and other factors
on NICE decisions. (forthcoming)

8. 4. Role of EQ-5D in NICE’s HTA Process
• NICE requests that its HTA
include an analysis of incremental
cost effectiveness
• NICE methods guide (2008)
states a clear and strong
preference for a single
instrument: EQ-5D
• But acknowledges that
– EQ-5D data not always
available
– EQ-5D may not always be the
appropriate measure

9. 5. Collecting and Analysing EQ-5D
Data to Inform HTA
• EQ-5D can be included alongside disease specific PROs in clinical
trials and observational studies
– E.g. baseline; 4 weeks; 8 weeks; 12 weeks
– More frequent data collection also feasible, e.g. daily patient diary
– Which is appropriate will depend on the nature of the disease and
intervention
• Various modes of data collection possible, e.g. paper and pencil;
web-based; PDA; telephone
• Data can be used to test for statistically significant improvements
in outcomes compared to placebo and/or relevant comparators
• Results used to populate health economics models

10. Daily Reports of EQ-VAS for Multiple
Sclerosis Patients
Source: Parkin, et al. (2004) Use of a VAS in a daily patient diary. Soc Sci Med 59:351-360.

11. 6. Utility Weighting EQ-5D Profiles
– the Role of Value Sets
• Patients self-report their health on EQ-5D
• For the purposes of HTA, and estimating QALYs, patients‘
EQ-5D ‗profiles‘ are summarised by a single number, on a
scale anchored at 1 (full health) and 0 (dead)
• These QoL weights/‘values‘ come from ‗value sets‘, based on
preferences of the general public
• ‗Stated preferences‘: questions (eg. TTO, DCE, SG) to
indicate how good or bad health states are from the
perspective of members of the general public (imagining
living in EQ-D states)

12. Applying Value Sets to EQ-5D Profile Data
Measuring health on a
generic HR-QoL instrument:
the EQ-5D
www.euroqol.org/

13. York ‘MVH’ Model, Based on TTO Valuations of EQ-5D Health States
Attribute 12122 32111 12233
Constant -0.03 -0.03 -0.03 -0.03
Mobility level 2 -0.066
Mobility level 3 -0.271 -0.271
Self care level 2 -0.029 -0.029 -0.029 -0.029
Self care level 3 -0.097
Usual activities level 2 -0.127 -0.127
Usual activities level 3 -0.224
Pain & discomfort level 2 -0.144 -0.144
Pain & discomfort level 3 -0.376 -0.376
Anxiety & depression level 2 -0.114 -0.114
Anxiety & depression level 3 -0.259 -0.259
Any level 3 -0.305 -0.305 -0.305
Sum of utility loss -.317 -0.635 -1.126
Value of health state 0.683 0.365 -0.126
Source: Dolan (1996) Medical Care

14. The Role of Values in Statistical Analysis
• Index values within a data set are the product
of both the data being analysed (the profile)
and externally provided data (the weights)
• Health state index data are therefore the result
of information both on the people whose
health has been measured and on those
whose values have been measured
• Parkin, Rice and Devlin (2010): weights
introduce an exogenous source of variance
which affects statistical inference using the
index data
• Implication: important to use
appropriate, local value sets in HTA

15. Example from a Recent Clinical Trial
of Treatments for OAB
Source: Pavesi, Devlin, Hakimi, Herdman, Nazir, Odeyemi. (2013) Understanding the effects on HR-QoL of treatment for
overactive bladder: a detailed analysis of EQ-5D clinical trial data for mirabegron. (forthcoming).

18. 7. What if EQ-5D Data Are Not
Available?
• Mapping from a disease specific PRO to EQ-5D is
possible.
• Various methods available (direct vs. indirect)
• NICE DSU recommendations on mapping
• Mapping is subject to some important challenges and
limitations…
“Where relevant EQ-5D data are not available, then another solution would be to
map from another measure of HRQL or disease severity that has been used in
relevant studies and to predict EQ-5D responses from statistical mapping
functions. These can be estimated from other data sets containing both
instruments. This strategy is accepted by NICE in the absence of EQ-5D
data, but it is always second best to the direct use of EQ-5D and may come with
a penalty of increased uncertainty.”

19. 8. Is EQ-5D Always Appropriate?
• EQ-5D shown to be a reliable and sensitive measure of
patient reported outcomes in many disease areas—but not
all
• EQ-5D may not always be the appropriate measure:
lacking sensitivity; ‗missing‘ descriptive items
• For a recent review, see NICE DSU report on EQ-5D

20. Evidence on EQ-5D: Some Examples
Hearing
Schizophrenia
Bipolar disorder
Vision
Depression and anxiety
Some cancers
Skin
Personality disorder

21. What Can Be Done when EQ-5D Is
Shown Not to Be Appropriate?
1. Generic preference-based measure (e.g. SF-6D or HUI3)—but
concerns about comparability between generic measures
2. Condition specific preference-based measure—more concerns
about comparability between QALYs generated by different
instruments (e.g. for dementia, cancer, asthma and so on)
due to focusing effects, comorbidities, side-effects
Develop extra ‘bolt on’ dimensions
for EQ-5D?

22. What Are the Issues for
Bolt-On Development?
1. Determine candidate dimensions
2. Develop labels and levels
3. Psychometric testing
4. Test impact on health state values
5. Test impact on the form of the preference function for
whole EQ-5D – are bolt-ons simply additive?
6. Estimating value sets/functions with new bolt-ons

23. 9. Concluding Remarks
• From 2014 in UK: Value Based Pricing (VBP)
– Changing (expanded) role for NICE
– QALYs (and EQ-5D) highly likely to be central to
assessments of the value of new technologies in VBP
• Plans to introduce VBP have further highlighted questions
about what the ‗cost effectiveness threshold‘ should be.
– Debate over what methods best identify the opportunity
cost
• To ensure budgets used efficiently (produce as much health as
possible) important to look at value for money of both new
and old technologies
• Measurement of outcomes in ‗real world‘ settings will become
increasingly important

24. For additional information, please contact Prof Nancy Devlin at
ndevlin@ohe.org
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