The role of patient reported outcome (PRO)
data in HTA: issues in measuring and
valuing health
Professor Nancy Devlin
Director of Research
OHE
PSI conference, May 16th 2017
Content
1. Patient reported outcomes (PROs) – what do
they measure? Why are they important?
2. Descriptive systems and profile data
• Analysing profile data
3. Patients’ assessments of overall health
4. Scoring and values/utilities
• Sources/properties of values; implications for
statistical analysis of utilities
5. Differences between the 3L and 5L; implications
for HTA
Promoting Statistical Insight (PSI) conference, London
May 2017
Promoting Statistical Insight (PSI) conference, London
May 2017
1.PROs: what do they measure?
• The goal of most health care is to improve patients’
health
• Patients arguably the best judge of how they feel
“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)
• PROs: aim to capture patients’ subjective account of
their own health in a structured way.
-
Psychometric properties: validity; reliability
• PRO data a useful complement (not substitute) for
clinical endpoints.
Promoting Statistical Insight (PSI) conference, London
May 2017
Types of PROs
How is health described?
How is health
summarised?
Generic
Condition-specific
Scores
Eg. SF-36
Eg. Oxford Hip Score, Oxford
Knees Score (literally thousands
of such PROs exist)
Values/utilities Eg. EQ-5D
HUI
SF-6D
AQOL
15D
• Mapping to preference based
measures (various methods)
• Some condition specific
instruments accompanied by
value sets eg. EORTC-8D,
derived from the EORTC QLQC30 forInsight
use(PSI)
in patients
with
Promoting Statistical
conference, London
May 2017
cancer
Uses of PROs
• Clinical trials
• Observational studies
• Population health surveys
• Routine collection in health service
delivery, eg.
• ‘PROMs’ in English NHS
• UK private health care sector (PHIN)
• Alberta Health Services, Canada
• New Zealand Southern Cross.
Promoting Statistical Insight (PSI) conference, London
May 2017
EQ-5D
descriptive
system
and
profile
Promoting Statistical Insight (PSI) conference, London
May 2017
EQ-VAS:
Patients’
overall
assessment
of their
health
Promoting Statistical Insight (PSI) conference, London
May 2017
Utilities
• Single number summaries of how good or bad
each health state is
• Anchored at 1= full health, 0 = dead, < 0 worse
than dead (as required by QALYs)
• Based on stated preferences of general public
considering hypothetical states
•
With exceptions eg. Sweden’s TLV prefers patient’s
values
•
Both could be argued to be relevant:
“health economic guidelines could require analysis of benefit
in terms of QALYs based on both patient and general public
preferences” (Versteegh & Brouwer 2017)
Promoting Statistical Insight (PSI) conference, London
May 2017
Utility/happiness
• What EQ-5D and other
PROs measure is nested
within broader concepts of
QoL and utility.
Quality of life
Health-related
quality of life
Health
status
• Conceptually, the borders
between HR-QoL and QoL
and between QoL and
utility are not precisely
defined.
• EQ-5D profile captures specific aspects of HR-QoL
• EQ-VAS (a ‘feeling thermometer’) which captures the patients’ overall
assessment of how good or bad their health is;
• Utilities (values) which capture the general public’s affective forecasts of
utility in those states
• Increasing interest in broader concepts: social care outcomes; subjective
wellbeing and ‘happiness’
Promoting Statistical Insight (PSI) conference, London
May 2017
Profiles: simple descriptive analysis
• Nature of profiles observed + value set characteristics drive
observed distribution of utilities
• eg. in these 3L data, no level 3 observed on mobility
• ‘N3’ term on 3L utilities important characteristic of utility
Promoting Statistical Insight (PSI) conference, London
May 2017
Pareto classification of health change
Comparing a patient’s PRO profile at any 2 points
in time:
-
The same
-
Better on at least 1 dimension, no worse on
others = improvement
-
Worse on at least 1 dimension, and no better
on others = worsening
-
Better on some dimensions, worse on others =
mixed
Promoting Statistical Insight (PSI) conference, London
May 2017
What is the EQ-VAS measuring?
Promoting Statistical Insight (PSI) conference, London
May 2017
4. Scores and utilities
• Act to weight the levels/dimensions in profiles
so they can be aggregated into a single number
•
Preference based (values/utilities) or nonpreference based (scores)
•
Utilities: 1= full health, 0 = dead, < 0 worse
than dead
•
Neither are ‘neutral’ – there is no objective way
of aggregating profile data
•
Which value set is used introduces an
exogenous source of variance, and can bias
statistical inference.
Promoting Statistical Insight (PSI) conference, London
May 2017
What causes the distribution of
observed utilities?
Promoting Statistical Insight (PSI) conference, London
May 2017
5L value set
Both papers free to download: www.ohe.org
Promoting Statistical Insight (PSI) conference, London
May 2017
EQ-5D-5L valuation protocol
Promoting Statistical Insight (PSI) conference, London
May 2017
Comparing 3L and 5L value sets
UK 3L: Dolan 1997
0
0
.5
.5
1
Density
Density
1
1.5
2
1.5
UK ‘Crosswalk’ van Hout et al 2013
0
.5
1
-.5
value
0
.5
1
2
value
.5
1
1.5
England 5L: Devlin
et al 2016
0
Density
-.5
-.5
0
.5
1
value
Promoting Statistical Insight (PSI) conference, London
May 2017
Comparison with 3L and ‘crosswalk’
% health states
worse than dead
Preferences
regarding
dimensions
(ordered from most to
least important in terms
of level 5 weight)
Value of 55555
(33333)
Value of 11112*
Value of 11121*
Value of 11211*
Value of 12111*
Value of 21111*
Minimum value
Maximum value
5L value set
Crosswalk value set
3L value set
5.1%
(159 out of 3,125)
Pain/Discomfort
26.7%
(833 out of 3,125)
Pain/Discomfort
34.6%
(84 out of 243)
Pain/Discomfort
Anxiety/Depression
Mobility
Mobility
Mobility
Anxiety/Depression
Anxiety/Depression
Self-care
Self-care
Self-care
Usual Activities
Usual Activities
Usual Activities
-0.285
-0.594
-0.594
0.922
0.937
0.950
0.950
0.942
-0.285
0.879
0.837
0.906
0.846
0.877
-0.594
0.848
0.796
0.883
0.815
0.850
-0.594
1.000
1.000
1.000
Promoting Statistical Insight (PSI) conference, London
May 2017
Implications of the results
• The EQ-5D-5L value set for England has a lower range of values
than the current UK EQ-5D-3L value set
• Proportion of states with negative values is considerably lower
• Implies that treatments for very severe conditions generate smaller
gains than is currently assumed
– e.g. interventions that reduce the level of anxiety/depression from
extreme to severe
• Treatments seeking to alleviate pain/discomfort and
anxiety/depression are highly valued and most likely to be
prioritised
• Mobility is less influential than before
• Life-extending treatments may generate larger gains than is
currently assumed
Promoting Statistical Insight (PSI) conference, London
May 2017
Concluding remarks
•
PRO data are often under-analysed.
•
Be aware of the nature of the underlying profile
data in your sample
•
Look at patients’ EQ-VAS data
•
Be aware of the characteristics of the value set (or
scoring system) used to summarise profiles –
results may be affected by it (sensitivity analysis)
•
Understand the normative issues determining
choice of value set, and whether relevant to your
study (eg if not estimating QALYs, utilities may not
be the most relevant)
Promoting Statistical Insight (PSI) conference, London
May 2017