OHE hosted a Lunchtime Seminar that examined both the approach and the results of research to date. OHE's Koonal Shah presented his research, which was critiqued by Dr Rachel Baker of Glasgow Caledonian University. Both presentations are included in this file.

1. Valuing Health at the End of Life
Koonal Shah, OHE
Rachel Baker, Glasgow Caledonian University
OHE Lunchtime Seminar
26 March 2013 • London

2. Research and Findings to Date
Koonal Shah
Office of Health Economics
OHE Lunchtime Seminar
26 March, 2013 • London

3. Study Team and Note on Funding
• This research is a collaboration between Koonal Shah (Office of
Health Economics) and Allan Wailoo, Aki Tsuchiya and Arne Risa
Hola (all University of Sheffield)
• The research was funded by the National Institute for Health and
Clinical Excellence (NICE) through its Decision Support Unit (DSU)
• The views, and any errors or omissions, expressed in this
presentation are the authors’ only

4. NICE End of Life Criteria
• Criteria that need to be satisfied for NICE’s supplementary end-
of-life policy to apply are currently as follows.
The treatment is indicated for patients with a short life
C1 expectancy, normally less than 24 months
There is sufficient evidence to indicate that the treatment offers
C2 an extension to life, normally of at least an additional three
months, compared to current NHS treatment
The treatment is licensed or otherwise indicated, for small patient
C3 populations

5. Overview of Project
Study 1: Exploratory study
• Aim: to pilot an approach to eliciting priority setting preferences
• Aim: to explore the rationales underpinning people’s stated preferences
• Small scale (n=21); convenience sample; face-to-face interviews
Study 2: Preference study
• Aim: to test whether there is public support for giving priority to end of life treatments
• Aim: to validate the approach and worth of conducting a large scale study
• Medium scale (n=50); representative sample; face-to-face interviews
Study 3: Discrete choice experiment
• Aim: to examine people’s preferences regarding end of life more robustly
• Aim: to examine the extent to which people are willing to sacrifice overall health in order to
give priority to end of life treatments
• Large scale (n=3,969); representative sample; web-based survey

6. Findings from Preliminary Studies
• Elicitation approach found to be feasible
• No consensus set of preferences
• Majority wished to give priority to the end-of-life patient, but a
sizeable minority expressed the opposite preference
• ‘No preference’ rarely expressed
• Strong preference for treatments the improve quality of life
• Preferences appear to be driven by how long patients have known
about their illness (i.e. how long they have to ‘prepare for death’)
• People are happy to prioritise based on characteristics of
patients/disease/treatment when gains to all patients are equal in
size … next step is to understand the extent to which they would
sacrifice health gain to pursue equity objectives

7. DCE Study
• DCEs (discrete choice experiments) elicit people’s preferences
based on their stated preferences in hypothetical choices
• Surveys comprise several ‘choice sets’, each containing competing
alternative ‘profiles’ described using defined ‘attributes’ and a
range of attribute ‘levels’
• Respondents’ choices between these profiles are analysed to
estimate the contribution of the attributes to overall utility

8. Attributes and Levels
Attribute Unit Levels
Life expectancy without treatment months 3, 12, 24, 36, 60
Quality of life without treatment % 50, 100
Life expectancy gain from treatment months 0, 1, 2, 3, 6, 12
Quality of life gain from treatment % 0, 25, 50
• Concept of ‘50% health’ was explained as follows:
‘Suppose there is a health state which involves some health problems. If
patients tell us that being in this health state for two years is equally
desirable as being in full health for one year, then we would describe
someone in this health state as being in 50% health’.

9. Study Design
• Forced choices (no ‘neither A nor B’ option)
• Generic descriptions of patients, illnesses and treatments
• Steps taken to avoid bias due to task order or possibility of
respondents reverting to default choices
• 10 standard DCE tasks, followed by two ‘extension tasks’ designed
specifically to explore whether respondents’ choices are influenced
by information about how long the patient has known about their
illness

12. Web-Based Surveys
Pros Cons
• Can recruit a vey large sample • No guarantee that respondents have
quickly and cheaply listened to or understood
• Avoids interviewer bias instructions
• Survey highly customisable – e.g. • Concerns about effort and
randomisation procedures engagement
• Quality control procedures can be • High level of drop out
put into place • Limited debriefing opportunity
• Any less likely to be representative • Concerns about representativeness
than other modes of administration? of sample

13. Background Characteristics
# % gen pop %
Total 3,969 100 100
Gender
Male 1,942 49 49
Female 2,027 51 51
Age
18-24 404 10 11
25-44 1,413 36 38
45-64 1,228 31 31
65+ 924 23 21
Social grade a
A 221 6 4
B 1,114 28 22
C1 1,150 29 29
C2 645 16 21
D 357 9 15
E 482 12 8

14. Background Characteristics (2)
# %
Household composition
With children 963 24
Without children 3,006 76
Education
No education beyond minimum school leaving age 889 22
Education beyond minimum school leaving age; no degree 1,244 31
Education beyond minimum school leaving age; degree 1,836 46
Self-reported general health level
Very good 1,008 25
Good 1,958 49
Fair 770 19
Poor 210 5
Very poor 23 1
Experience of close friends of family with terminal illness
Yes 2,689 68
No 1,197 30
Question skipped by respondent 83 2

15. Results
• Best fitting model included main effects plus three interactions:
• LE without treatment against LE gain
• Rationale: small gains in life expectancy may be increasingly important when life
expectancy without treatment is short
• LE without treatment against QOL gain
• Rationale: whether a quality of life improvement or a gain in life expectancy is
preferred may depend on life expectancy without treatment
• LE gain against QOL gain
• Rationale: the important of a gain in life expectancy may depend on whether it is
accompanied by a quality of life improvement

16. Attribute / level Coefficient p-value
Interaction: LE without treatment # LE gain
12 months # 1 months -0.1715 0.15
12 months # 2 months -0.4220 0.00
12 months # 3 months -0.1633 0.18
12 months # 6 months -0.7294 0.00
12 months # 12 months -0.6039 0.00
Attribute / level Coefficient p-value 24 months # 1 months -1.1308 0.00
LE without treatment 24 months # 2 months -1.0782 0.00
24 months # 3 months -0.8614 0.00
3 months [baseline] - -
24 months # 6 months -1.2413 0.00
12 months 0.1755 0.12 24 months # 12 months -1.2601 0.00
24 months 0.9307 0.00 36 months # 1 months -0.7280 0.00
36 months 0.7841 0.00 36 months # 2 months -1.0428 0.00
60 months 1.2625 0.00 36 months # 3 months -1.2252 0.00
QOL without treatment 36 months # 6 months -1.6695 0.00
36 months # 12 months -1.3963 0.00
50% [baseline] - -
60 months # 1 months -1.3159 0.00
100% 0.6730 0.00 60 months # 2 months -1.4933 0.00
LE gain 60 months # 3 months -1.2558 0.00
0 months [baseline] - - 60 months # 6 months -2.0434 0.00
1 month 0.1855 0.08 60 months # 12 months -1.7114 0.00
2 months 0.8517 0.00 Interaction: LE without treatment # QOL gain
12 months # 25% 0.4562 0.00
3 months 1.0855 0.00
12 months # 50% 0.2139 0.00
6 months 2.0433 0.00 24 months # 25% 0.2734 0.00
12 months 2.9381 0.00 24 months # 50% 0.4123 0.00
QOL gain 36 months # 25% 0.8457 0.00
0% [baseline] - - 36 months # 50% 0.7374 0.00
60 months # 25% 0.5379 0.00
25% 0.0632 0.47
60 months # 50% 0.6676 0.00
50% 1.0212 0.00 Interaction: LE gain # QOL gain
1 months # 25% 0.7649 0.00
1 months # 50% 0.5254 0.00
2 months # 25% 0.3197 0.00
2 months # 50% 0.3543 0.00
3 months # 25% 0.6321 0.00
3 months # 50% 0.3163 0.00
6 months # 25% 0.6661 0.00
6 months # 50% 0.2744 0.00
12 months # 25% 0.3466 0.00
12 months # 50% [baseline] - -

17. Transforming into Predicted Probabilities
• Following the approach used by Green and Gerard* we calculated
the relative predicted probabilities for all of the 110 profiles
• This allows us to compare the profiles that are likely to be most
preferred overall with those that are likely to be least preferred
overall
• The predicted probability of alternative i being chosen from the
complete set of alternatives (j=1,…,J) is given by:
𝑃𝑃𝑛𝑛𝑛𝑛 = 𝑗𝑗 = 1, … , J
𝑒𝑒 𝑉𝑉 𝑛𝑛𝑛𝑛
∑J
𝑉𝑉
𝑗𝑗=1 𝑒𝑒 𝑛𝑛𝑛𝑛
* Green, C. and Gerard, K. (2009) Exploring the social value of health care interventions: A stated
preference discrete choice experiment. Health Economics. 18(8), 951-976.

18. Estimated Utility Score and Predicted
Probability of Choice for All Profiles
Rank Rank – LE without QOL without LE gain QOL gain Utility Prob. Cumul.
- best main treatment treatment (%) (mths) (%) Prob.
fitting effects (mths)
model model
1 1 60 50 12 50 4.17809 0.0155 0.0155
2 2 36 50 12 50 4.08461 0.0154 0.0309
3 3 24 50 12 50 4.04235 0.0153 0.0462
4 5 3 50 12 50 3.95938 0.0152 0.0614
5 4 12 50 12 50 3.74493 0.0148 0.0762
6 20 3 100 12 0 3.61116 0.0145 0.0908
- - - - - - - - -
105 107 36 50 1 0 0.24171 0.0029 0.9870
106 109 12 50 1 0 0.18955 0.0028 0.9898
107 110 3 50 1 0 0.18553 0.0028 0.9926
108 104 60 50 1 0 0.13213 0.0026 0.9952
109 94 3 50 0 25 0.06320 0.0025 0.9977
110 108 24 50 1 0 -0.01452 0.0023 1.0000

19. Levels of QALYs without Treatment /
Gains Associated with All 110 Profiles
6
5
4
3
QALYs
2
1
0
0.0023 0.0040 0.0055 0.0062 0.0072 0.0085 0.0100 0.0112 0.0120 0.0130 0.0140
-1
Standardised predicted probability of being chosen
QALY without QALY gain Linear (QALY without) Linear (QALY gain)

20. Most and Least Preferred Profiles
LE without QOL without LE gain QOL gain (%) QALYs QALYs gained
treatment treatment (mths) without
(mths) (%) treatment
10 most preferred 27 55 11 38 1.14 1.76
55 most preferred 27 57 7 31 1.27 1.22
55 least preferred 27 65 2 10 1.49 0.29
10 least preferred 28 50 1 3 1.18 0.06

21. Subgroup Analysis
• We defined a selection of respondent subgroups whose choices
may be expected to differ from those of the rest of the sample
• Respondents with experience of close friends or family with terminal illness
• Respondents with responsibility for children
• Respondents who voluntarily left open-ended comments
• Respondent who completed the survey unusually quickly
• We found no substantial differences between the results for any of
these subgroups and those for the full sample

22. Categorising According to ‘Choice Strategy’
% choices made Number (%) of respondents who…
Choice strategy according to this never followed this sometimes followed always followed this
strategy strategy this strategy strategy
Choose patient with larger
QALY gain
0.75 1 (0.0%) 3,530 (88.9%) 438 (11.0%)
Choose patient with larger
LE gain
0.69 20 (0.5%) 3,405 (85.8%) 544 (13.7%)
Choose patient with fewer
QALYs without treatment
0.47 182 (4.6%) 3,701 (93.2%) 86 (2.2%)
Choose patient with less
LE without treatment
0.45 355 (8.9%) 3,434 (86.5%) 180 (4.5%)
• Multinomial logit regressions used to identify driving factor(s) behind
respondents’ membership of the ‘always / never choose patient with fewer
QALYs without treatment’ subgroup
• Marginal effects of age and health satisfaction were found to be statistically
significant, but both are small in practical terms
• As age increases, the probability of always choosing the patient with fewer QALYs
without treatment decreases, but even a 30-year increase in age would not be
sufficient for a 1% decrease in this probability

23. Extension Tasks
• Extension tasks showed that including information about the
amount of time that patients have known about their prognosis has
a clear impact on preferences
• Holding everything else constant, respondents are less likely to
choose to treat a patient if that patient has known about their
illness for two years than if they have only just found out about it
• Caveat: focusing effect may exaggerate importance

24. Summary of Findings
• Choices driven by size of health gain
• Concern about the extent to which the patient is at the end of life
appears to have a negligible effect
• Overall view seems to be that giving higher priority to those who
are worse off is desirable, but only if the gains from treatment are
substantial
• No evidence of public support for giving higher priority to end-of-
life treatments than to other types of treatments if the health gains
offered by the treatments being ‘de-prioritised’ are larger than
those offered by the end-of-life treatments

25. Caveats and Limitations
• Small range of scenarios covered – all involve poor prognoses
(some people might consider 5 years to be ‘end of life’)
• Does not necessarily refute evidence elsewhere in the literature
that people wish to pursue equity concerns
• Great deal of preference heterogeneity
• Limited opportunities for feedback and debriefing – cannot know
for certain the extent to which the choice data truly reflect
respondents’ beliefs and preferences (or whether there were
adopting heuristics)
• Framing effects clearly exist in stated preference studies

26. To enquire about additional information and analyses, please contact Koonal Shah at
kshah@ohe.org
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©2013 OHE

27. Institute for Applied Health Research
and
Institute for Society and Social Justice Research
Valuing health at the end of life
Shah et al
Discussion
Rachel Baker
Reader in Health Economics
rachel.baker@gcu.ac.uk
Yunus Centre for Social Business & Health

29. MRC Methodology panel
Are health gains for terminally ill patients more valuable? Measuring
societal views on health care resource allocation
Rachel Baker, Neil McHugh, Helen Mason, Cam Donaldson,
Laura Williamson, Jon Godwin, Marissa Collins (GCU)
Job van Exel (Erasmus, Rotterdam)
Cathy Hutchinson (Beatson Cancer Centre, NHS Greater Glasgow &Clyde)

30. Outline
• Why this work is important
• Strengths, limitations and questions:
– Study design
– Methods
– Findings/ conclusions
• Future research…
– MRC end of life Q methodology study

31. Are equal sized health gains ‘worth’ the same
regardless of who benefits and in what ways?

34. Rawlins et al Brit J of
Clinical Pharmacology 2010
• “The Institute recognises that the public,
generally, places special value on treatments
that prolong life – even for a few months – at
the end of life, as long as that extension of life is
of reasonable quality (at least pain-free if not
disability-free). NICE has therefore provided its
advisory bodies with supplementary advice
about the circumstances under which they
should consider advising, as cost-effective,
treatments costing >£30,000 per QALY.” p 348

35. Study Design
• Carefully considered, rigorous design
– Preliminary and pilot work
• Choice based stated preference study
– Ordering effects and other biases controlled
– Questions blocked by choice type
• Web-based questionnaire
– Diagrams and text explanation
– Pilot tested and soft-launch

36. Methods 1: Question Framing
• Choice between two patients A and B
• Described in terms of 4 attributes
– LE and QoL without treatment
– LE and/or QoL gains with treatment
• Individuals rather than groups of patients
• QALY gain (green area)
– How is QoL gain treated/ interpreted?
• Indifference option (either not neither)

38. Methods 2: Informed, C onsidered Responses
• Choice types and questions of dominance
– 13 Choice types (see table 4)
– Both patients have same LE and QoL; without treatment
one patient gains more LE and QoL (11)
• 10% respondents failed the dominance test.
– Simple error?
– Plausible rationale?
• Excluding them from the analysis did not
make any difference

39. Methods 2: Informed, Considered Responses
• Some choices between a patient who is worse off and
gains more from treatment and a patient who is
better off and gains less
• ?Not strictly dominated? QALY maximising choice
and concern for severity are the same
• 40% respondents (or in 30% of choices) chose the
patient who was better off and gained less
• Why?
• Qualitative research/ cognitive interviewing

40. Methods 2: Informed, Considered Responses
• Evidence of deliberation and carefully considered
choices .. .in web based research
– Lots of typed comments/ explanations?
– Taking time over the survey
• Speedsters!
– Problem of web-based surveys
– question of cut off...
– < 3 mins for intro, 12 DCE questions and demographics
– Quickest pilot respondent, employed/educated
people with interviewer present, 6 minutes

41. Findings 1
• Large respondent sample, lots of observations
– Any ‘representative’ sample is problematic
• Reporting of ‘raw’ data (and choice types) as well
as modelling helpful
– Table 4 (add majority choice for clarity?)
– Main effects model (table 5) shows increasing
value placed on bigger gains and
– Increasing value placed on patients with
better health without treatment (odd?)

42. Findings 2
• Main effects with 3 interactions
– Model fits better
– Table 6 is difficult to interpret…
– Instead of coefficients of attribute levels, Table 7:
110 profiles ranked according to probability of
choice
– ? Including interactions seem to take care of
‘oddness’? And untreated profile has little effect on
choice (but 40% of ‘those choice types’ are still
odd?)
– Choices driven by QoL and LE gains

43. Findings 3
• Table 8 and figures 5 and 6 summarise the
untreated QALYs and QALY gains on probability of choice
• Choice is driven by QALY gains and not untreated profile
– Add to table 7 for all 110?
– QALY gains relatively small?
– Very few levels on QoL
– Replication with different attribute levels?
• Similar to DCE findings from SVQ study
– Although modelled differently

44. Consider adding info about QALY gain
to full rank pred prob table 7?
Rank Rank LE QOL LE gain QOL gain Utility Prob. Cumul.
- best – with/ without (mths) (%) Prob.
fitting main t treatme
model effect treat nt (%) QALY gain
s (mths
mode )
l
(5*.5)+(1*1)
1 1 60 50 12 50 =3.5 4.17809 0.0155 0.0155
2 2 36 50 12 50 2 4.08461 0.0154 0.0309
3 3 24 50 12 50 2 4.04235 0.0153 0.0462
4 5 3 50 12 50 1.125 3.95938 0.0152 0.0614
5 4 12 50 12 50 1.5 3.74493 0.0148 0.0762
6 20 3 100 12 0 1 3.61116 0.0145 0.0908
- - - - - - - - -
105 107 36 50 1 0 0.04 0.24171 0.0029 0.9870
106 109 12 50 1 0 0.04 0.18955 0.0028 0.9898
107 110 3 50 1 0 0.04 0.18553 0.0028 0.9926
108 104 60 50 1 0 0.04 0.13213 0.0026 0.9952
109 94 3 50 0 25 0.06 0.06320 0.0025 0.9977

47. Findings 4: Extension Tasks
• 8 DCE choices selected
• Information about prior knowledge of disease added
– ?different respondents?
• Responses to the extension task questions, compared with
DCE responses suggest that time since diagnosis
is important
– We found the same in qualitative work (although not sure how
important relative to other things)
• Indifference option (either, not neither)
– Might have helped with issue of focus and
extension questions

48. Overall
• Well conducted piece of research
• Raises questions about NICE end of life policy
• Quality of life and life extension are most important
• Replication/ future research
– Stretch the attributes over a wider set of levels
• Esp LE without treatment
• Qol levels?
– Draw comparisons against patients who are
less severely ill
– Cognitive interviewing/ qualitative work and methods
to understand rationale for ‘odd choices’