Valuing Health at the End of Life: Defining Public Preferences

Valuing Health at the End of Life
A Stated Preference Discrete Choice Experiment

Koonal Shah
Heath Economics Research Centre Seminar Series
University of Oxford • 21 January 2014

Study team and note on funding
• This study is a collaboration with Allan Wailoo and Aki
Tsuchiya (both University of Sheffield)
• The study was funded by the National Institute for Health
and Care Excellence (NICE) through its Decision Support
Unit
• The views, and any errors or omissions, expressed in this
presentation are those of the authors only

Valuing health at the end of life (HERC seminar)
21/01/2013 2

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:

C1

The treatment is indicated for patients with a short
life expectancy, normally less than 24 months

C2

There is sufficient evidence to indicate that the
treatment offers an extension to life, normally of at
least an additional three months, compared to current
NHS treatment

C3

The treatment is licensed, or otherwise indicated for,
small patient populations

21/01/2013 3

NICE end-of-life criteria (2)
• Placing additional weight on survival benefits in patients
with short remaining life expectancy could be considered a
valid representation of society's preferences
• But the NICE consultation revealed concerns that little
scientific evidence supports this premise

21/01/2013 4

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
21/01/2013 5

Scenario 1
Both patients are same age today (Time=0)
Time
(years)

0

1

2

3

4

5

6

7

8

9

10

11

Patient A

Patient B

denotes time in full quality of life
denotes life extension (at full quality of life) achievable from treatment

21/01/2013 6

Scenario 2
Patient B is 9 years older than patient A today
Time
(years)

0

1

2

3

4

5

6

7

8

9

10

11

Patient A

Patient B

21/01/2013 7

Scenario 3
Both patients are same age today
Time
(years)

-9

-8

-7

-6

-5

-4

-3

-2

-1

0

1

A

B

21/01/2013 8

2

Scenario 4
Both patients are same age today (30 years old)
Time
(years)

0

1

2

A

B
denotes life extension (at 50% quality of life) achievable from treatment

denotes improvement from 50% quality of life to full quality of life
achievable from treatment

21/01/2013 9

Scenario 5
Both patients are same age today (70 years old)
Time
(years)

0

1

2

A

B

21/01/2013 10

Scenario 6
Patient B is 9 years older than patient A today
Time
(years)

0

1

2

3

4

5

6

7

8

9

10

A

B

21/01/2013 11

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
• Preference for treatments that 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
21/01/2013 12

DCE study
• DCEs elicit people’s preferences based on their stated
preferences given 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

21/01/2013 13

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’.
21/01/2013 14

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
patients have known about their illness

21/01/2013 15

21/01/2013 16

21/01/2013 17

Web-based surveys
Pros
•
•
•

•
•

Can recruit a vey large
sample quickly and cheaply
Avoids interviewer bias
Survey highly customisable –
e.g. randomisation
procedures
Quality control procedures
can be put into place
Any less likely to be
representative than other
modes of administration?

Cons
•

•
•
•
•

No guarantee that
respondents have read or
understood instructions
Concerns about effort and
engagement
High level of drop out
Limited debriefing
opportunity
Concerns about
representativeness of sample

21/01/2013 18

Background characteristics
#
Total
Gender
Male
Female
Age
18-24
25-44
45-64
65+
Social grade
A
B
C1
C2
D
E

%

3,969

100

gen pop
%
100

1,942
2,027

49
51

49
51

404
1,413
1,228
924

10
36
31
23

11
38
31
21

221
1,114
1,150
645
357
482

6
28
29
16
9
12

4
22
29
21
15
8

21/01/2013 19

Background characteristics (2)
#
963
3,006

Household composition
With children
Without children
Education
No education beyond minimum school leaving age
Education beyond minimum school leaving age; no degree
Education beyond minimum school leaving age; degree
Self-reported general health level
Very good
Good
Fair
Poor
Very poor
Experience of close friends or family with terminal illness
Yes
No
Question skipped by respondent

%
24
76

889
1,244
1,836

22
31
46

1,008
1,958
770
210
23

25
49
19
5
1

2,689
1,197
83

68
30
2

21/01/2013 20

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
21/01/2013 21

Attribute / level
LE without treatment
3 months [baseline]
12 months
24 months
36 months
60 months
QOL without treatment
50% [baseline]
100%
LE gain
0 months [baseline]
1 month
2 months
3 months
6 months
12 months
QOL gain
0% [baseline]
25%
50%

Coefficien p-value
t
0.1755
0.9307
0.7841
1.2625

0.12
0.00
0.00
0.00

0.6730

0.00

0.1855
0.8517
1.0855
2.0433
2.9381

0.08
0.00
0.00
0.00
0.00

0.0632
1.0212

0.47
0.00

Attribute / level
Coefficient
p-value
Interaction: LE without treatment # LE gain
0.15
-0.1715
12 months # 1 months
0.00
-0.4220
0.18
-0.1633
0.00
-0.7294
0.00
-0.6039
0.00
-1.1308
0.00
-1.0782
0.00
-0.8614
0.00
-1.2413
0.00
-1.2601
0.00
-0.7280
0.00
-1.0428
0.00
-1.2252
0.00
-1.6695
0.00
-1.3963
0.00
-1.3159
0.00
-1.4933
0.00
-1.2558
0.00
-2.0434
0.00
-1.7114
Interaction: LE without treatment # QOL gain
0.00
0.4562
12 months # 25%
0.00
0.2139
12 months # 50%
0.00
0.2734
24 months # 25%
0.00
0.4123
24 months # 50%
0.00
0.8457
36 months # 25%
0.00
0.7374
36 months # 50%
0.00
0.5379
60 months # 25%
0.00
0.6676
60 months # 50%
Interaction: LE gain # QOL gain
0.00
0.7649
1 months # 25%
0.00
0.5254
1 months # 50%
0.00
0.3197
2 months # 25%
0.00
0.3543
2 months # 50%
0.00
0.6321
3 months # 25%
0.00
0.3163
3 months # 50%
0.00
0.6661
6 months # 25%
0.00
0.2744
6 months # 50%
21/01/2013 22
0.00
0.3466
12 months # 25%
12 months # 50% [baseline]

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 =

𝑒𝑒 𝑉𝑉 𝑛𝑛𝑛𝑛

𝑉𝑉
∑J
𝑒𝑒 𝑛𝑛𝑛𝑛
𝑗𝑗=1

𝑗𝑗 = 1, … , J

* Green, C. and Gerard, K., 2009. Exploring the social value of health care interventions: a
stated preference discrete choice experiment. Health Economics, 18(8), pp. 951-976.
21/01/2013 23

Estimated utility score and predicted
probability of choice for all profiles
Rank
- best
fitting
model

Rank –
main
effects
model

LE without
treatment
(mths)

QOL without
treatment
(%)

LE gain
(mths)

QOL gain
(%)

Utility

Prob.

Cumul.
Prob.

-

-

60
36
24
3
12
3

-

50
50
50
50
50
100

-

12
12
12
12
12
12

-

50
50
50
50
50
0

-

4.17809
4.08461
4.04235
3.95938
3.74493
3.61116

0.0155
0.0154
0.0153
0.0152
0.0148
0.0145

0.0155
0.0309
0.0462
0.0614
0.0762
0.0908

0.0029
0.0028
0.0028
0.0026
0.0025

0.9870
0.9898
0.9926
0.9952
0.9977

110

108

24

50

1

0

0.0023

1.0000

1
2
3
4
5
6

105
106
107
108
109

1
2
3
5
4
20

107
109
110
104
94

36
12
3
60
3

50
50
50
50
50

1
1
1
1
0

0
0
0
0
25

-

0.24171
0.18955
0.18553
0.13213
0.06320
0.01452

-

-

21/01/2013 24

Levels of QALYs without treatment /
gains associated with all 110 profiles
6
5

QALYs

4
3
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)

21/01/2013 25

Most and least preferred profiles
LE
without
treatment
(mths)

QOL
LE gain
without
(mths)
treatment
(%)

QOL gain
(%)

QALYs
QALYs
without
gained
treatment

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

10 most
preferred

21/01/2013 26

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

•

Respondents 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
21/01/2013 27

Categorising according to ‘choice strategy’
Number (%) of respondents who…

% choices made
according to this
strategy

never followed this
strategy

sometimes followed
this strategy

always followed this
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%)

Choice strategy

•

•

Multinomial logit regressions used to identify driving factor(s) behind
respondents’ membership in the subgroup ‘always / never choose patient
with fewer QALYs without treatment’
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
21/01/2013 28

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 who has know about
their illness for two years than if the patient has only just
found out about their illness
• Caveat: focusing effect may exaggerate importance

21/01/2013 29

Extension tasks (2)

•

The above figure shows the impact on choices of providing information on
how long patients have known about their illness, summed across all 16
extension tasks
21/01/2013 30

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 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 ‘deprioritised’ are larger than those offered by the end of life
treatments

21/01/2013 31

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

21/01/2013 32

About OHE
To enquire about additional information, please contact Koonal Shah at kshah@ohe.org
To keep up with the latest news and research, subscribe to our blog, OHE News
Follow us on Twitter @OHENews, LinkedIn and SlideShare
The Office of Health Economics is a research and consulting organisation that has been providing
specialised research, analysis and expertise on a range of health care and life sciences issues and
topics for more than 50 years.
OHE’s publications may be downloaded free of charge for registered users of its website.
Office of Health Economics
Southside, 7th Floor
105 Victoria Street
London SW1E 6QT
United Kingdom
+44 20 7747 8850
www.ohe.org
©2013 OHE

21/01/2013 33

Valuing Health at the End of Life: Defining Public Preferences

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Andere mochten auch

Andere mochten auch (11)

Ähnlich wie Valuing Health at the End of Life: Defining Public Preferences

Ähnlich wie Valuing Health at the End of Life: Defining Public Preferences (20)

Mehr von Office of Health Economics

Mehr von Office of Health Economics (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Valuing Health at the End of Life: Defining Public Preferences