Nancy Devlin's presentation to the Royal Statistical Society on 18 June 2015

1. Professor Nancy J. Devlin
Office of Health Economics
Royal Statistical Society
June 18th 2015
Measuring and ‘valuing’ patient
reported health

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1. Measuring patient reported health
2. Use and applications of PROs
3. The role of PROs in economic evaluation
4. ‘Weighting’/summarising PROs: an example
(the EQ-5D-5L value set for England)
5. Statistical issues relating to the use of weights
6. Normative issues relating to the use of weights
7. Concluding remarks
Contents

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1. Measuring patient reported health
• Clinical measures of health (e.g. mortality rates;
diagnostics) can provide important evidence about
effectiveness and quality of health care.
• But these things miss the patients’ perspective on
health. Most health care has as its aim to make the
patient feel better. Growing awareness of the
importance of this.
• Patient reported outcomes (PROs) are questionnaires
that aim to measure patients’ subjective accounts of
their health in a structured, systematic way, that is
valid and reliable.
• Amenable to cross sectional and longitudinal analysis

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PRO instruments
“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]
• Many well-validated instruments exist which are reliable, sensitive
and widely used. (Oxford University website)
• Simple to complete; quick to analyse.
• Repeated observations (e.g. before and after treatment) can provide
a clear picture of changes in health, and outcomes from treatment.
• Condition specific PROMs: more question items/response options;
focussed on a specific aspect of health.
• Generic PROMs: measure health related quality of life generally.
Enable comparisons of health across conditions/health services. E.g.
“EQ-5D” and “SF-36”

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A generic PRO: the EQ-5D-5L
• Descriptive
system/’profile’
• 55 = 3,125 ‘states’
• Patients self-reported
health, which is
summarised in descriptive
terms as 11111, 55555,
etc.
• Methods of analysis:
descriptives; ‘level sum
score’; ‘Pareto
Classification of Health
Change’.

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The EQ-VAS - The EQ-VAS –
used to obtain
the patients’
overall
assessment of
their health.
- Simple to
analyse.

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2. Use and applications of PROs
Data collection Uses
Clinical trials Effectiveness & cost
effectiveness
Observational studies Effectiveness & cost
effectiveness
Population health surveys Burden of disease
Individual patients Personal health diaries; shared
decision making
Routine data collection as part of
health service delivery
- English NHS
- Private hospitals in the UK
- Sweden, Canada…
Monitoring quality of services
Provider performance
Effectiveness/cost effectiveness
of treatments

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4. PRO data in economic evaluation
• In cost effectiveness analysis, the incremental cost
effectiveness ratio (ICER) =  cost / QALYs .
Enables comparisons of ‘cost per QALY gained’ of
different treatments competing for funding.
• QALYs: A measure of outcome which combines both
quality and length of life.
• Quality of life used to ‘weight’ length of life
• Weights on a scale anchored at 1 = full health, 0 =
dead (< 0 ‘worse than being dead’)
• 1 QALY = a year of perfect health
• Can capture changes in quality of life, length of life
or both.

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5. Weighting/valuing PROs
• For use in economic evaluation, each health state
described by a PRO requires a QoL weight, anchored
on a scale anchored at 0 = dead and 1 = full health.
• Weights are obtained from stated preference studies
– a sample of respondents asked to consider a set of
health states that are hypothetical to them, and
engage in a series of tasks intended to discover how
good or bad they consider each to be
• Regression analysis used to model a ‘value set’ for all
health states

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EQ-5D-5L value set for England
• Research protocol developed by the EuroQol Research Foundation
• Stated preference data collected in face-to-face computer-
assisted personal interviews
• n = 1000 members of the adult general public of England,
selected at random from residential postcodes
• Sample recruitment sub-contracted to Ipsos MORI
• Each respondent valued 10 health states using TTO, randomly
assigned from 86 health states in an underlying design; and
seven DCE tasks, randomly assigned from 196 pairs of states
• ‘Composite’ TTO approach: conventional TTO for values > 0 and
‘lead time’ TTO for values < 0
• The EuroQol Valuation Technology software (EQ-VT) was used to
present the tasks and to capture respondents’ responses

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TTO for values > 0
(states better than dead)
Example shown:
U(hi) = 5/10 = 0.5
U(hi) = (x/t)
where x is the time in
full health and t is the
time in health state hi at
the respondent’s point of
indifference

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Example shown:
U(hi) = (5-10)/10
= -0.5
t = 20 years
lead time (LT) = 10 years
U(hi) = (x-LT)/(t-LT)
= (x-10)/10
Min value = -1
TTO for values < 0
(states worse than dead)

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DCE task

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The
resulting
EQ-5D-5L
value set
model
Note: The value set
reported here The value
set reported here has
‘interim’ status, until
such point as it is
accepted for publication
in a peer reviewed
journal. Please do not
use or quote these
results without
permission of the
presenting author.
England EQ-5D-5L values 95% CIs
constant 1.003 (0.983 - 1.019)
Mobility slight 0.057 (0.043 - 0.075)
moderate 0.075 (0.057 - 0.093)
severe 0.208 (0.190 - 0.227)
unable 0.255 (0.237 - 0.275)
Self-care slight 0.058 (0.045 - 0.074)
moderate 0.083 (0.061 - 0.101)
severe 0.176 (0.157 - 0.197)
unable 0.208 (0.189 - 0.225)
Usual activities slight 0.048 (0.033 - 0.066)
moderate 0.067 (0.047 - 0.086)
severe 0.165 (0.147 - 0.180)
unable 0.165 (0.152 - 0.184)
Pain/discomfort slight 0.059 (0.042 - 0.075)
moderate 0.080 (0.059 - 0.098)
severe 0.245 (0.225 - 0.264)
extreme 0.298 (0.278 - 0.317)
Anxiety/depression slight 0.073 (0.058 - 0.089)
moderate 0.099 (0.079 - 0.119)
severe 0.282 (0.263 - 0.298)
extreme 0.282 (0.267 - 0.300)

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EQ-5D-5L value set for England
Example: the value for health state
23245
constant 1.003
Constant
=1.003
Mobility = 2 0.057
Minus MO level 2
-0.057
Mobility = 3 0.075
Mobility = 4 0.208
Mobility = 5 0.255
Self-care = 2 0.058
Self-care = 3 0.083
Minus SC level 3
-0.083
Self-care = 4 0.176
Self-care = 5 0.208
Usual activities = 2 0.048
Minus UA level 2
-0.048
Usual activities = 3 0.067
Usual activities = 4 0.165
Usual activities = 5 0.165
Pain/discomfort = 2 0.059
Pain/discomfort = 3 0.080
Pain/discomfort = 4 0.245
Minus PD level 4
-0.245
Pain/discomfort = 5 0.298
Anxiety/depression = 2 0.073
Anxiety/depression = 3 0.099
Anxiety/depression = 4 0.282
Anxiety/depression = 5 0.282
Minus AD level 5
-0.282
State 23245 = 0.288
EQ-5D-5L
values for
England:
a worked
example

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6. Statistical issues re: use of weights
• Generic PROs like EQ-5D-5L use ‘utilities’ to summarise data
i.e weighting dimensions/levels.
• Condition specific PROs usually use ‘scores’ – a simple
summing up of points for each item
• There is no ‘neutral’ way of summarising patients’ PRO data.
• The weights are used introduce an exogenous source of
variance into statistical inference
Parkin D, Rice N, Devlin N. (2010) Statistical analysis of
EQ-5D profiles: does the use of value sets bias inference?
Medical Decision Making)
• Judgements made by researchers about which data to
include/exclude, how to model the value sets, can have a non-
trivial impact on the weights.

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7. Normative issues re: use of
weights
• Current approaches to weighting EQ-5D are driven
by the requirements of economic evaluation/QALYs
• Who – usually ‘the general public’ (apart from
Sweden, which prefers ‘experience based
utilities’ from patients.
• How – ‘utility’-based approaches (but what
underlying theory is relevant is disputable)
• SG = expected utility theory; TTO = Hicks utility
theory; DCE = random utility theory; VAS?
• Other methods; other theories (eg minimisation
of regret? Prospect theory?)

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Concluding remarks
• The QoL weights for PROs like EQ-5D have been
dictated by the requirements of cost effectiveness
analysis i.e. estimation of QALYs.
• The weights are sensitive to decisions made by
researchers about how to model stated preference
data.
• The weights are often used, in other applications, to
summarise PRO data, because it is convenient. But
results will be effected by the characteristics of the
value sets/weights used.
• Develop and promulgate other ways of summarising
PRO data, and encourage sensitivity analysis.