Professor Olivia Wu and Professor Neil Hawkins; Economics of Precision Medicine Workshop, University of Glasgow, 20th January 2017.

1. Improving the Focus of
Precision Medicine: the Role of
Health Technology Assessment

3. Programme
• The Scottish ecosystem for precision medicine
Professor Dame Anna Dominiczak
• Overcoming the barriers to precision medicine
Professor Richard Barker OBE
• The role of health technology assessment in precision medicine technologies
Professor Olivia Wu
• Key issues on the economics of precision medicine
Dr Paula Lorgelly
• A case study – rheumatoid arthritis
Professor Iain McInnes

4. Improving the Focus of
Precision Medicine: the Role of
Health Technology Assessment

5. Research at HEHTA
• Economic evaluation alongside clinical trials
• Decision analytical modelling
• Evidence synthesis
• Analysis of linked health data
• Incorporating perspectives and experiences
• Economics of population health
• Global HTA
• Economics of precision medicine
www.glasgow.ac.uk/hehta
@HEHTAGlasgow

6. Value of Precision Medicine
• Genomic (and other diagnostic) tests provide information that can
provide value
• Value of that information relates to the improvement in health that
results from acting on that information
• Need to quantify:
• Change in practice that results from the information generated by
a new test
• Health implications that result from change in practice

7. Measuring and Valuing Health:
Quality Adjusted Life-Years (QALYs) gained
Time (Years)
Quality of
life scale
(0-1)
0
1
8
Health profile with
intervention
Health profile
without
intervention
Quality
adjusted
life years
gained

8. Steps in the Economic Analysis
1. Define the ‘status quo’ in terms of expected cost and
expected health outcomes (QALYs)
2. Specify how diagnostic information could lead to
differential clinical practice for patient population (clinical
utility)
3. Translate clinical utility into QALY gains
4. Balance costs to give the expected value of the genomic
information

9. HEHTA’s Economics of Precision Medicine Projects
• Health economic components of the Glasgow Molecular Pathology
Node - demonstration projects in oncology, inflammation,
cardiovascular disease (funded by the MRC)
• Prioritising individuals for preventive statin therapy (funded by the
MRC)
• Developing a fully Bayesian framework for the identification and
estimation of subgroup effect in randomised controlled trials (funded
by the MRC)

10. The role of health technology
assessment in the successful
development of precision medicine
technologies
Neil Hawkins, Professor of Health Technology Assessment

11. Sources of value in precision medicine
Garau M et al.
Assessment of co-dependent technologies.
OHE Research Paper 12/03
Health economics for stratified medicine, 5th October 2016
urces of value from PGx
Value
1. Reducing
drug
adverse
effects
2. Reducing
time delays
in selecting
optimal Tx
3.Increasing
adherence
or
willingness
to start Tx
4. Enabling Tx
effective in a
small fraction
to be made
available
5.Reducing
uncertainty
about value

12. Successful development: NPV >0
Net Present Value (NPV)
=
(Achieved Price - Costs of Goods)
×
Market Size
×
Probability of Approval
–
Cost of Development
http://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/the-microeconomics-of-personalized-medicine

13. Health Technology Assessment:
a set of tools for exploring value
“Health technology assessment (HTA) refers to the the
systematic evaluation of properties, effects, and/or impacts of
health technology. It is a multidisciplinary process to evaluate
the social, economic, organizational and ethical issues of a
health intervention or health technology”

14. “Supply Side” and “Demand Side” HTA
Supply-side HTA Demand-side HTA
Timing During development (“Early”) Peri and post approval (“Late”)
Objective Maximise returns on R&D investment Maximise health benefit
Informs decisions regarding Investment, product placement, and study
design
Usage, reimbursement and further
research
Evidence base Fluid. Little specific to the technology Fixed (conditional re-imbursement)
Clinical decision space Defined by the target product profile Defined by the evidence base and
license
Fundamental Decision Rule Fund programmes with greatest NPV Reimburse when net health benefit is
greater than zero
HTA Resources Constrained. Multiple candidate
technologies to consider. Time limited
Committed resources. Limited number
of technologies reviewed

15. Identification of responders in acute disease
• Examples
• Anti-bacterial sensitivity in febrile children; strep throat test
• Value Drivers
• Health and cost impacts of ineffective treatment
• Test performance: costs of false negatives and false positives
• Compliance

16. Identification of patients at risk of adverse events
• TPMT Genotyping for Azathioprine; HLA-A testing for carbamazepine (epilepsy)
• Value drivers:
• Actual “baseline” rate of adverse events
• Test performance
• Health impacts (and costs) of adverse events
• Effectiveness (and costs) of treatment alternative

17. Identification of responders in chronic disease
• Testing for Ritux responders in RA
• Value drivers
• Can responders be identified empirically
• What are the costs and health impacts of trialling drugs
• What are the costs of false positives

18. In addition to value,
we also need to think about uptake
Net Present Value (NPV)
=
(Achieved Price - Costs of Goods)
×
Market Size
×
Probability of Approval
–
Cost of Development
(all discounted to current value)
http://www.mckinsey.com/industries/pharmaceuticals-and-medical-products/our-insights/the-microeconomics-of-personalized-medicine

19. Uptake: Roger’s Model of the diffusion of innovation
Diffusion of Innovations, 5th Edition by Everett M. Rogers

20. Case Study: an RNA test for bacterial infections in
febrile children

21. Seems to perform well

22. However, life is
a little more complicated

23. Leading to many questions
• Is the test (cost-)effective?
• Will it be used? Will clinicians/parents trust it? What evidence
will be required?
• How will it be used? To replace current tests or as an adjunct?
• Who will it be used for?
• What is the value of test? How do we value the externalities of
anti-bacterial use?
• What are the financial incentives for developers? For payers?

24. Do you agree with McKinsey’s assessment of
potential value?
Davis et al. The microeconomics of personalized medicine. McKinsey Quarterly, Feb 2010