These slides are a short presentation of our work for the Department of Health for England on eliciting societal preferences for burden of illness. For full details our report at: seehttp://www.eepru.org.uk/VBP%20survey%20research%20report.pdf.

1. Making Value-Based Pricing A
Reality: Issue Panel
Moderator: Meindert Boysen
Panelists: John Brazier, Roberta
Ara and Werner Brower
ISPOR 16th Annual European Congress 2-6 November 2013, The
Convention Centre in Dublin, Eire

3. Value-based pricing: wider
considerations
• There is a ‘basic’ NHS cost per QALY threshold
• Costs and QALYs (through weighting) to take into account:
– diseases with greater ‘burden of illness’ as reflected in
QALY loss from a condition
– greater therapeutic innovation and improvement (size of
QALY gain)
– wider societal benefits (e.g. productivity and carer time)
• Basic threshold adjusted to reflect the opportunity cost of
displaced activities weighted using same methods
• Price negotiated on the basis of the cost per weighted QALY
compared to the new threshold (from 2014)

4. Comparing new and displaced treatments in VBP:
Expression as an adjusted cost per QALY threshold
Adjustment to c/Q threshold:
£25,000 *
New drug
1+ 30%+ 0.1
= £24,138
1+ 20%+ 0.25
Other use (?)
OR
X
Cost:…………………………………………………………………………..
£50k
(£50k displaced)
£25k (measured ICER) £25k (centre of threshold range)
Cost / QALY:……………………………………………………………………..
->QALYs gained:…………………………………………………………………..
2 gained
2 lost
=
BoI weight:…………………………………………………………………… +20%
+30%
->Weighted QALYs:…………………………………………………………………
2.6
2.4
WSBs, £:…………………………………………………………………….
£12,000
£30,000
->WSBs, QALYs:…………………………………………………………………
0.2 QALYs worth
0.5 QALYs worth
-> Total Benefits:…………………………………………………………………
2.8 QALYs’ worth gained
< 2.9 QALYs’ worth lost
X

5. Elicitation of societal preferences for
Burden of Illness, Therapeutic
Improvement and End of Life from a UK
online panel
John Brazier
DH PRU in Economic Evaluation of Health and Care
Interventions (EEPRU), University of Sheffield
Donna Rowen, Clara Mukuria, Sophie Whyte, Anju Keetharuth, Aki Tsuchiya, Phil Shackley
Health Economics and Decision Science, ScHARR, University of Sheffield
Arne Risa Hole
Economics Department, University of Sheffield
Acknowledgements: Angela Robinson (University of East Anglia) and Gavin Roberts (DH)

6. Outline of presentation
•
•
•
•
•
Value-based pricing: BOI, TI and EOL
Methods
Main results
Weights for use in DH framework
Discussion

7. Elicitation of societal preferences
Discrete choice experiment (DCE) survey using online
UK panel to elicit societal preferences for:
• Burden of illness (QALY loss from condition)
• Therapeutic improvement (size of QALY gain from
treatment)
• End of life (e.g. NICE weights QALY gain more where
expected survival is 24 months and survival gain 3
months or more)

8. 100%
Conceptual framework
Normal population
Health
Dead
Today
Normal life
expectancy
Life expectancy from today

9. 100%
Conceptual framework
Normal population
Health
Health
without
treatment
Dead
Today
Without treatment
Life expectancy
without treatment
Life expectancy from today
Normal life
expectancy

10. 100%
Conceptual framework
Normal population
Health
gain
Treatment gain
Health
Health
without
treatment
Dead
Today
Without treatment
Life expectancy
Survival gain
without treatment
Life expectancy from today
Normal life
expectancy

11. Main survey design
• Internet panel sample – allows for large numbers, collection fast
Survey content
•
•
•
•
Introduction video played
2 practice and 10 real DCE questions
9 questions asking general attitudes assessed in survey
17 questions on ‘you and your health’ and understanding
Design
• 4 normal life expectancies (5, 20, 40, 80 years)
• Both small and large starting point and gains in health and
survival
• 580 pairs selected using D-efficient design. Impossible scenarios
not included
• 58 ‘card blocs’ in total across 4 normal life expectancies

12. 1

13. FEEDBACK

14. Modelling
• U=f(QALY gain, QALY gain squared, EOL or BOI)
• Estimation by conditional logit regression model
• Dependent variable = Choice patient group A or
patient group B
• Estimated for pooled data and each of the 4
separate normal life expectancies
Basic additive model:
V = β1 QALY + β2 QALY2 + β3 BOI (or EOL)
Where a positive β2 would suggest TI

15. Marginal rate of substitution
The marginal rate of substitution between BOI and QALY
(or EOL and QALY) provides a measure of the weight of
BOI in terms of QALY gain equivalents
e.g. MRS1 = -β3 /β1
MRS2 = -β3 /(β1+ 2*β2QALY)
So MRS2 varies by size of QALY

16. Main results (1)
Sample
• 3669 respondents (55% response rate)
• Similar age, but more females and unemployed
respondents and less healthy than UK norm
Practice questions
• PQ1 – Majority chose larger QALY gain (90.7-92.5%)
• PQ2 - No evidence of preference for higher BOI
(46.8% - 54.3%)

17. Regression results
VARIABLES
All
5 yrs
20 yrs
40 yrs
80 yrs
QALY
0.276***
3.641***
0.751***
0.404***
0.171***
QALY_sq
-0.004***
-0.709***
-0.037***
-0.014***
-0.002***
BOI
0.017***
0.12***
-0.000
0.039***
0.005**
VARIABLES
All
5 yrs
20 yrs
40 yrs
80 yrs
QALY
0.281***
3.229***
0.761***
0.400***
0.175***
QALY_sq
-0.004***
-0.602***
-0.037***
-0.014***
-0.002***
EOL
0.609***
0.607***
0.375***
0.576***
0.314***

18. Overview of results
Regression results:
• QALYs matter but at a decreasing rate – no
support for TI
• BOI matters – but is weak and inconsistent
• EOL is significant
• Coefficients change for different variants of
normal life expectancy

19. Weights for BOI
Model (1): Assuming the
value of a QALY is constant
• MRS(1) of 1 more unit of
BOI is -0.040 QALYs
Warning: This is additive and
not proportionate to the
size of QALY gain
MRS(2)
0.05
- 0.063
0.1
Model (2) Allowing value of
a QALY to vary
QALY
gain
- 0.063
0.5
- 0.063
1
- 0.064
2
- 0.066
5
- 0.073
10
- 0.087
20
- 0.141

20. Limitations
•
•
•
•
Limited range of characteristics (e.g. no age)
Online data collection
Additive design
Robustness - many respondents may have continued
to make the mistake of assuming the profiles were
for them even after feedback
– Identified respondents who chose a profile with smaller
QALY gain and lower BOI but larger number of lifetime
QALYs
– Once these were excluded (n=2247) then BOI coefficients
were all positive, significant and larger than for the whole
sample
• Weights – choice of variant and specification

21. To download the report go to:
http://www.eepru.org.uk/VBP%20survey%20res
earch%20report.pdf