valuing paediatric preference-based measures: using a discrete choice experiment with duration to...
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Valuing a paediatric preference-based measure: the CHU-9D-NL
Donna Rowen
School of Health and Related Research (ScHARR)
University of Sheffield
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Project team
• Brendan Mulhern (University of
Technology Sydney, Australia)
• Katherine Stevens (University of Sheffield,
UK)
• Erik Vermaire (TNO, Netherlands)
• Acknowledgements: Richard Norman
(Curtin University)
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Introduction• Child valuation
• Valuing the CHU-9D-NL in the
Netherlands
• Outline the CHU-9D
• Normative decisions
• Methodology
• Results
• Comparison to CHU-9D-UK value set
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Child valuation
• Whose values?
• Adult valuation arguments often focus around whether
values should be elicited from general population or
patients
• For children the argument is less around experience of
the health state and more around the population –
General population? Adolescents?
• Which perspective?
• Elicitation technique and mode of administration?
• Comparability with adult values for consistency
and combined use in HTA e.g. vaccinations
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CHU-9D• CHU-9D is a paediatric preference-based
measure of quality of life (Stevens, 2009;2010)
• Developed using qualitative interviews with
over 70 school children aged 7-11 in the UK
• Dimensions and wording selected using the
transcripts and Framework analysis
• 9 dimensional self-completed measure
• Translated into 6 languages including Dutch
• Suitable for self-report in ages 7-17 years
• Used in over 180 studies to date
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CHU-9D ClassificationDimension Wording Severity levels
Worried I don’t feel worried today A little bit / a bit / quite / very
Sad I don’t feel sad today A little bit / a bit / quite / very
Pain I don’t have any pain today A little bit / a bit / quite a lot / a lot
Tired I don’t feel tired today A little bit / a bit / quite / very
Annoyed I don’t feel annoyed today A little bit / a bit / quite / very
School work
/homework
I have no problems with my
schoolwork / homework
today
A few problems / some problems
/ many problems / can’t do
Sleep Last night I had no problems
sleeping
A few problems / some problems
/ many problems / can’t sleep
Daily routine I have no problems with my
daily routine today
A few problems / some problems
/ many problems / can’t do
Able to join in
activities
I can join in with any
activities today
Most / some / a few / no
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Existing value sets
• UK – adults using standard gamble (SG)
(Stevens, 2012)
• Australia – adolescents using best-worst scaling
– adults using best-worst scaling
(BWS)
(both anchored using time trade-off)
(Ratcliffe et al, 2012;2015;2016)
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Whose values?
Adult preferences
• Tax payers
• Understanding of tasks
• Able to answer questions involving ‘dead’
• Do not necessarily reflect child or young adolescent preferences
Child and adolescent preferences
• Children and adolescents experience the health states
• Adolescents have understanding of some tasks e.g. BWS, DCE
• Children 7-11 unlikely to fully understand tasks
• Are adolescent preference weights more appropriate for 7-11 year
olds than adult preferences?
• Unable to answer questions involving ‘dead’ so require adult (or
young adult) data e.g. standard gamble or time trade-off to anchor
the states on 1-0 full health-dead scale
• Is this preferable to using only adult values?
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Perspective?If asking adults, they could be asked to imagine:
• The health state in the context of a 10 year old child
• Which child matters
• Will incorporate respondents views about children and child
health (may think it is much worse for a child to be sick, may
not want to sacrifice years of life for a child)
• The health states for themselves as a child
• Recall bias, also some of the concerns raised above
• The health state for themselves
• ‘Veil of ignorance’, value is not influenced by respondents
views about children and child health (comparability)
• If society values child health more, QALY weighting or
deliberation could be used at decision level for HTA e.g. NICE
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CHU-9D-NL valuation
• Whose values?
• Perspective?
• Elicitation technique and mode of
administration?
• Influenced by choice of population and
perspective
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CHU-9D-NL valuation
• Whose values?
Adult general population sample
• Perspective?
Themselves
(reworded school work/homework dimension
to work/house work)
• Elicitation technique and mode of
administration?
Online DCE with duration
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The survey and sample• Participants recruited via existing online panel,
paid via points from market research agency
• Information sheet, informed consent
• Sociodemographic questions
• CHU-9D and EQ-5D-5L
• 1 practice DCE plus 12 DCE questions
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Selecting profiles• Profiles selected using Ngene software taking
into account regression model specifications
• 3 dimensions fixed across both profiles in a pair,
built into design
• Duration of 1, 4, 7, 10 years – successfully used
previously for other surveys
• Selected 204 choice sets and allocated each to
one of 17 blocks of 12 for each survey version
using a D-Optimal design
• Choice sets randomly ordered within a block for
each participant but dimension order fixed
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Example questionHealth description A Health description B
You live for 10 years with the following then you die:
You live for 1 year with the following then you die:
You feel a little bit worried You feel a little worried
You feel a bit sad You feel very sad
You have a bit of pain You don’t have any pain
You feel quite tired You feel quite tired
You feel quite annoyed You don’t feel annoyed
You can’t do work/housework You have many problems with yourwork/housework
You have a few problems sleeping You can’t sleep at all
You can’t do your daily routine You have a few problems with yourdaily routine
You can join in with any activities You can join in with any activities
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Modelling DCE with duration dataModel specification (Bansback et al, 2012):
𝜇𝑖𝑗 = 𝛼𝑖 + 𝛽1𝑡𝑖𝑗 + 𝛽′2𝐱𝑖𝑗𝑡𝑖𝑗 + 𝜀𝑖𝑗
𝜇𝑖𝑗 represents the utility of individual 𝑖 for profile j
𝑡𝑖𝑗 represents time
𝛽1 is the coefficient for duration in life years t
𝛽′2 represents the coefficients on the 36 interaction terms of duration
and attribute levels
• Anchored using the Marginal Rate of Substitution
• Divide through by the duration coefficient: 𝛽2𝑖𝑗
𝛽1
• Conditional logit model with robust standard errors
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The sampleSample
n=1,276
%
Netherlands
n=16,979,120
%
Male 49.8% 49.8%
Age under 3016.5
18.7
30-3915.4
15.3
40-49 18.8 18.9
50-59 17.0 17.7
60+ 32.2 29.4
Employed 56.0 53.6
Married 63.8 62.3
EQ-5D-5L NL
Mean (s.d.)
0.795 (0.230) 0.869 (0.170)
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Utility decrements
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Utility decrements for UK and The Netherlands
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Regression models
First model NL DCE UK SG (OLS)
Statistically
significant
31 (out of 37) 30 (out of 36)
Incorrect sign 2 0
Inconsistencies 4 14
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ExamplesYou feel a little bit worriedYou feel a little bit sadYou have a little bit of painYou feel a little bit tiredYou feel a little bit annoyedYou have a few problems with your work/houseworkYou have a few problems sleepingYou have a few problems with your daily routineYou can join in with most activities
You feel very worriedYou feel very sadYou have a lot of painYou feel very tiredYou feel very annoyedYou can’t do work/houseworkYou can’t sleep at allYou can’t do your daily routineYou can join in with no activities
State 111111111
NL = 0.788
UK = 0.679
State 444444444
NL = -0.568
UK = 0.326
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Robustness• Models re-estimated excluding:
• All responses less than 5 seconds
• All responses over 10 minutes
• Excludes 9.3% of responses
• Slightly larger coefficients
• Same problems with inconsistencies and
incorrect signs
• One exception that work levels 3 and 4 are consistent
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Discussion • Valuation of CHU-9D-NL using online DCE with duration
with adult general population sample feasible and
generated sensible results
• Large contrast in size of utility decrements to UK SG with adult
general population – with more consistent coefficients
• Problems of dimension framing and interpretation of
“work/housework” rather than “school work/homework”
• In the Netherlands income loss from being off work due to illness
is minimal
• Should child or adult preferences be used to value child
health states?
• What is the appropriate perspective?
• Does the use of ‘informed’ adult values offer a solution?
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Discussion
• Which values are most appropriate for informing
resource allocation decisions?
• Complication of generating QALYs from birth or toddlers
through to adulthood and beyond
• For comparability reasons could argue for use of adult
general population values elicited from own perspective
• Utility values are not affected by additional factors
such as views around child or child health
• Arguably is the health state that is important not who
experiences it or the cause
• Potentially raises issue of QALY weights or different
threshold
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References• Bansback N, Brazier J, Tsuchiya A, Anis A. Using a discrete choice experiment to estimate
health state utility values. J Health Econ. 2012;31(1):306-18.
• Ratcliffe J, Flynn T, Terlich F, Brazier J, Stevens K, Sawyer M. Developing adolescent
specific health state values for economic evaluation: an application of profile case best worst
scaling to the Child Health Utility-9D. Pharmacoeconomics 2012; 30:713-27.
• Ratcliffe J, Chen G, Stevens K, Bradley S, Couzner L, Brazier J, Sawyer M, Roberts R,
Huynh E, Flynn T. Valuing Child Health Utility 9D Health States with Young Adults: Insights
from A Time Trade Off Study. Applied Health Economics and Health Policy, 2015; 13:485-492
• Ratcliffe J, Huynh E, Stevens K, Brazier J, Sawyer M, Flynn, T. Nothing about us without us?
A comparison of adolescent and adult health-state values for the child health utility-9D using
profile case best-worst scaling. Health Economics, 2016; 25: 486-496
• Rowen D, Mulhern B, Stevens K, Vermaire E. Estimating a Dutch value set for the paediatric
preference-based CHU-9D using a discrete choice experiment with duration. HEDS
Discussion Paper 2017, University of Sheffield, available online.
• Stevens, K J. Working With Children to Develop Dimensions for a Preference-Based,
Generic, Paediatric Health-Related Quality-of-Life Measure. Qualitative Health Research.
2010; vol. 20: 340 - 351
• Stevens, K J. Developing a descriptive system for a new preference-based measure of
health-related quality of life for children. Quality of Life Research. 2009; 18 (8): 1105-1113
• Stevens K. Valuation of the Child Health Utility 9D Index. Pharmacoeconomics 2012; 30:8:
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