Generating preference-based values for the EQ-5D-Y to support its use in HTA

Koonal Shah, Office of Health Economics
Oliver Rivero-Arias, National Perinatal Epidemiology Unit,
Nuffield Department of Population Health, University of Oxford
NICE Technical Forum  29 January 2019
Generating preference-based
values for the EQ-5D-Y to support
its use in HTA

NICE Technical Forum, 29 January 2019
2
Acknowledgements
• This presentation reports selected methods and findings from
research supported by grants from the EuroQol Research
Foundation and the Spanish Department of Health
• The views expressed do not necessarily reflect the views of
the funders

3
Outline
• Introduction to measuring and valuing child health
• EQ-5D-Y
• Brief primer on QALYs, measurement and valuation
• Valuation challenges
• Findings from recent EQ-5D-Y valuation research
• Suggested points for discussion

4
Measuring and valuing child health
• Increasing research and HTA interest in measuring and valuing the
health of children and adolescents
• Measures available for measuring child health, e.g. PedQL, KIDSCREEN
• These measures provide summary scores across dimensions, but are
not preference-based
• Not suitable for use in the estimation of QALYs
• Generic preference-based measures developed specifically for use in
younger populations are also now available – HUI2, CHU9D
• EQ-5D-Y – ‘youth’ version of the EQ-5D

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EQ-5D-Y instrument
Developed in 2010 for use in children
and adolescents aged 8 to 15 years

6
Brief primer on QALYs, description
and valuation
• Quality-adjusted life years: a
measure of health captures both
quality of life and length of life
• Quality of life is used to ‘weight’
patients’ life years
• Quality of life scale is anchored at
1 = full health, 0 = dead
• 1 QALY = a year of perfect health
Time (Years)
Quality
of life
scale
(0-1)
0
1 Health profile of
a CF adolescent
with current
treatment
Mild State
Moderate State
Severe State
Transplant State
40

7
Brief primer on QALYs, description
and valuation
• The ‘quality adjustment’ in QALYs requires two pieces of
information:
1. A description of patients’ health (e.g. using self/proxy-reported EQ-
5D-Y health states)…
2. …and the valuation (on a scale anchored at 0 and 1) of those health
states

8
For every health
state, there is a
‘value’ on a 0-1
anchored scale
Obtaining ‘values’ for health states

9
Example of an EQ-5D ‘value set’

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How do we get the values?
• We typically ask members of the general public to
complete stated preference ‘choice-based’ exercises
• These involve considering a range of different health
states, imagining what it would be like to experience
them, and indicating how good or bad they are
• For decision making, we need a set of ‘values’ that
represents the overall view of the relevant population
• Preference elicitation techniques:
• Rating scale (RS)
• Standard gamble (SG)
• Time trade-off (TTO)
• Discrete choice experiment (DCE)

11
EQ-5D-Y: state of play
• >40 language versions available
• Use is modest but growing
• Demand for use in HTA, but no value sets to support that
• Recent research has indicated that regular EQ-5D-3L value sets
cannot be used for children and adolescents

12
Valuation challenges
• Normative issues (whose preferences should we elicit?)
• Perspective issues (whose health should we elicit the
preferences for?)
• Methods issues (how do we elicit the preferences, and on what
basis do we make this choice?)
• VAS values lower for EQ-5D-Y than for EQ-5D-3L (Kind et al., 2015)
• TTO values higher for EQ-5D-Y (Kreimeier et al., 2015) – possibly
due to reluctance to sacrifice life years for children

13
Whose preferences?
• Position adopted by decision makers in the UK, US, Netherlands
(amongst others): relevant preferences are those of the
general public
• EuroQol protocol: EQ-5D value sets should be based on the
preferences of the general public, not of the subgroup whose
health is being evaluated
• Reflects fact that HTA is intended to inform the broad allocation of
resources across an entire population / health system
• Public = taxpayers and potential users of health care
• Societal perspective, insurance principle, adaptation arguments

14
Who counts as the public?
• The UK is not prescriptive about who constitutes a member of
the public, but there seems to be an implicit consensus:
• Those who bear the cost of providing health care
• Those eligible to vote
• These criteria exclude children and adolescents

15
Alternative view
• Preferences of children and adolescents are relevant because
they are potential patients / users of the health care being
evaluated
• May be relevant to understand the preferences of children (as
patients) – could be relevant in other, non-HTA uses of the
instrument (Versteegh and Brouwer, 2016)
• Alternatives to conventional techniques now available that may
be suitable for eliciting the preferences of younger people

16
15. To put the above principles into practice on matters that affect children
and young people’s health and wellbeing, NICE has adopted the
following overarching aim:
• To involve children and young people, (and the organisations that
represent their interests), on matters pertinent to NICE’s work and
that affect children and young people’s health and wellbeing.
16. In pursuit of this aim, NICE is committed to:
• ensuring that the perspectives of children and young people –
including those who share the protected characteristics of the
Equality Act 20102 or live in disadvantaged circumstances – are
taken into account in relevant areas of NICE’s work
• producing guidance and standards on topics covering children and
young people’s health and wellbeing, which have been informed and
influenced by their views and experiences
Extract from NICE Patient and Public
Involvement Policy (pp.6-7)

17
Whose health?
• If we are to use adult preferences to value child health
instruments, whose health should they be valuing?
• Their own health?
• Their own health, imagining they are a child?
• The health of their own child?
• The health of an unidentified child?
– How old should the child be, given the range of ages covered by
different instruments?

18
What approaches have been used to develop value
sets of instruments for paediatric populations?
Instrument Country Sample Perspective Elicitation
method
QWB USA Adults Self RS
HUI Mark 2 Canada Adults Child perspective SG/RS
HUI Mark 3 Canada Adults Self SG/RS
16D Finland Adolescents Self RS
17D Finland Adults Child perspective RS
AQoL-6D Australia Adolescents Self TTO
AHUM UK Adults Self TTO
CHU-9D UK Adults Self SG
CHU-9D Australia Adolescents Self DCE
EQ-5D-Y USA Adults Child perspective DCE
QWB: Quality of Well-Being Scale; HUI: Health Utility Index; 16D: sixteen-
dimensional measure of health-related quality of life; 17D: seventeen-
dimensional measure of health-related quality of life; AQoL-6D: Assessment of
Quality of Life 6-Dimension; CHU-9D: Child Health Utility 9D; AHUM: Adolescent
Health Utility Measure; RS: rating scale, SG: standard gamble, TTO: time trade-
off; DCE: discrete choice experiment

19
Recent EQ-5D-Y valuation research
• Conduct a discrete choice experiment to obtain preferences for EQ-5D-Y
health states using a sample of adults. Values obtained on a latent scale
(not a QALY scale)
Study 1: Latent scale DCE study – adult version
Study 3: Anchoring study
• In parallel, test a range of methods for anchoring latent scale values at
0 = dead to convert latent scale of DCE values into QALY scale
• Not covered in this presentation
Study 2: Latent scale DCE study – adolescent version
• Replicate study 1 using a sample of adolescents

20
Study 1: Latent Scale DCE Adults
• Online discrete choice experiment (DCE) survey
• Participants belonging to an online panel: quotas used for
gender, age and social grade

21
• 1,000 adults (+18) completing the survey from
the perspective of a “10-year old child”
• We used a Blocked Bayesian efficient design to
identify 150 pairs to present to participants
• + fixed pair (one health state clearly dominant)
• Survey instrument included: consent/screening,
self-reported EQ-5D-Y, DCE exercise, debriefing,
background questions
• Data collection during February-March 2017

22
• Discrete choice modelling used to analysis
DCE data
• multinomial logit, scaled multinomial logit;
mixed logit; generalised multinomial logit and
latent class
• Relative attribute importance of model
parameters of EQ-5D-Y levels
• Participant engagement with the
elicitation task

23
Study 2: Latent Scale DCE Adolescents
• Same design of Study 1
• 1,005 adolescents (11-17 years old)
completing the survey from their own
perspective
• Data collection during February-March
2018

24
Selected background characteristics
Adolescents
(own health)
(n=1,005)
Adults
(child perspective)
(n=1,000)
Family affluence scale (FAS)
Low FAS score 29 (2.9%) -
Medium FAS score 456 (45.4%) -
High FAS score 519 (51.6%) -
Missing values 1 (0.1%)
Education
Did not continue education - 230 (23.0%)
Continued education (no degree) - 315 (31.5%)
Continued education (degree) - 455 (45.5%)
Experience of serious illness
In self - 312 (31.2%)
In friends or family - 629 (62.9%)
In caring for others - 279 (27.9%)
Self-reported health
EQ-5D-Y health state 11111 587 (58.4%) 148 (14.8%)
Other EQ-5D-Y health states 418 (41.6%) 852 (85.2%)
Samples were
representative
of the UK
general
population in
terms of age,
gender, grade
and nation

25
Relative attribute importance model
parameters of EQ-5D-Y levels
Levels
Adolescents
(own health)
Mean (95% CI)
Adults
(child perspective)
Mean (95% CI)
Statistically significant
differences in
preferences?
MO2 Some problems 3.7 (2.8 to 4.7) 3.2 (2.3 to 4.1)
MO3 Lot of problems 12.9 (11.9 to 13.9) 9.4 (8.3 to 10.5) *
SC2 Some problems 3 (2.2 to 3.8) 2.9 (2.1 to 3.6)
SC3 Lot of problems 10.2 (9.2 to 11.2) 7.7 (6.7 to 8.6)
UA2 Some problems 4.5 (3.8 to 5.3) 4.8 (4 to 5.5)
UA3 Lot of problems 12.1 (11.2 to 13.1) 11.6 (10.7 to 12.5)
PD2 Some pain 7.5 (6.5 to 8.4) 8.8 (7.9 to 9.7)
PD3 Lot of pain 21.1 (19.6 to 22.7) 24 (22.2 to 25.7) *
AD2 Bit worried 5.2 (4.3 to 6) 7.4 (6.5 to 8.4) *
AD3 Very worried 19.7 (18.1 to 21.3) 20.3 (18.7 to 21.9) *
MO: Mobility; SC: Self-care; UA: Usual activities; PD: Pain/Discomfort;
AD: Worried, sad or unhappy
Red figures indicate three parameters with largest contributions

26
Participant engagement with DCE
State 11122 vs 22233 Adolescents Adults
Proportion selecting state
11122
88.46% 89.5%
Adults (child perspective)Adolescents

27
Debriefing questions (freq.,%)
Adolescents Adults
Found the tasks difficult:
Strongly disagree 208 (20.70%) 217 (21.70%)
Disagree 307 (30.55%) 290 (29.00%)
Neither agree nor disagree 224 (22.29%) 226 (22.60%)
Agree 226 (22.49%) 224 (22.40%)
Strongly agree 40 (3.98%) 43 (4.30%)
Found difficult to imagine the
health problems described
Strongly disagree 105 (10.45%) 185 (18.50%)
Disagree 248 (24.68%) 329 (32.90%)
Neither agree nor disagree 213 (21.19%) 209 (20.90%)
Agree 369 (36.72%) 206 (20.60%)
Strongly agree 70 (6.97%) 71 (7.10%)

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• DCE did not include dead or a duration attribute, so values are
on a ‘latent’ (or undefined) scale
• Further info needed to rescale latent scale values so that they
are anchored at 0 and 1, as required for QALY calculations
• Which anchoring method should be used? Possible criteria:
• Feasibility
• Acceptability to decision makers
• Potential for administration online
• Theoretical and empirical coherence with the preference data to be
anchored
• Theoretical and empirical consistency with adult valuations in
use in HTA
Need for anchoring

29
Some discussion points
• Which perspective should be employed?
• Is the relevant and realistic to use adolescent preferences?
• How important is it that the methods used for child health health
valuation are commensurate with those used for adult health
valuation?
• Which stakeholders should feed into these decisions?

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To enquire about additional information and analyses, please contact
Koonal Shah at kshah@ohe.org
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