Technologies that enhance the precision and effect of therapies can make a critical contribution to ensuring value for money and improving patient care. Methods and processes for assessing value, however, still are imperfect. This presentation reviews the challenges and identifies some approaches for meeting them.

1. Assessing the Value of Co-dependent Technologies:
How Can Current Methods and Processes Be Improved?
ScHARR Seminar, University of Sheffield
16 April 2013 ● Sheffield, UK
Martina Garau, Office of Health Economics

2. Adrian Towse (OHE) and the other authors of:
Garau, M., Towse, A., Garrison, L., Housman, L. and Ossa, D.
(2012) Can and should value based pricing be applied to
molecular diagnostics? Personalized Medicine. 10(1), 61-72.
Acknowledgements

3. • What is the value of co-dependent technologies?
• Framework for assessing value
• How prove value?
• How aggregate value dimensions?
• Proposed institutional processes
• International experience
• Australia
• NICE
• Conclusions
Agenda

4. • “Technologies that are dependent on another technology
either to achieve their intended effect or to enhance their
intended effect” (www.health.gov.au)
• In particular, a diagnostic test (Dx) can be used to identify
patients most likely to:
• Respond or fail to respond to a drug treatment (Tx)
• Exhibit adverse events
But also to:
• Monitor responses to drugs
• Determine the risk of developing a disease
Definition of co-dependent technologies

5. 1. Key elements of value are:
• Health effects for patients (clinical effectiveness measured by the
QALY)
• Cost offsets (savings to the health care system)
What is the value of co-dependent technologies? (1)
Traditionally, ICERs do not capture benefits beyond health attributes
NHS,PSS Cost of Treatment A - NHS,PSS Cost of Treatment B
ICER =
Health effects of Treatment A - Health effects of Treatment B
The focus is on downstream effects of treatments not recognising the additional
value brought by use of Dx

6. • Other value dimensions
• Societal preferences giving priority to certain patients or
diseases
• Quality of life aspects not reflected in generic measures
used in CE analyses
• Other effects beyond those to patients and NHS
(productivity gains)
• Health care process related aspects (dignity, time and
location of treatment)
• Information for the patient independent of health
effects
What is the value of co-dependent technologies? (2)

7. • The value created is a “joint product” and there are no rules
for the attribution of the value to one or the other
• Garrison and Austin (2007) pointed out that how value is
allocated across patients, payers, Dx manufactures and Tx
manufacturers depends on the institutional context
• E.g. whether the Tx was priced before the Dx was available; the
relative strength of intellectual property protection for Dx and Tx
• This will have consequences in terms of incentives for
evidence generation and subsequent innovation
The issue of attribution of value of
co-dependent technologies

8. 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
Framework for assessing value of
co-dependent technologies
Value dimensions
derived from:
• Characteristics of
Dx recently
introduced
• Literature review
on the economics
of personalised
medicine and
value of
information of Dx

9. Availability of Dx can improve average benefit-risk ratio
so, depending on the severity of side effects:
• Tx obtains marketing authorisation, or
• Use of a licensed Tx in clinical practice increases
Example: HLA-B*5701
• Allele associated with hypersensitivity to abacavir for HIV-1
• Identification of the marker has increased prescribing of abacavir, which
now is recommended for HLA-B*5701-negative patients in European and
US guidelines
1. Reducing or avoiding drug adverse effects

10. Identifying non-responders and switching them to an alternative
treatment regime/care can:
• Improve survival and/or quality of life (particularly in diseases at advanced
stages)
• Avoid or reduce the cost of treating non-responders
• Avoid or reduce inconvenience to patients
Example: BCR-ABL
• Test identifies chronic myelogenous leukemia (CML) patients who are
receiving treatment, but not responding to it
• Can prevent the disease from progressing to blast crisis and death, and
enables stopping first-line treatment when no longer effective
2. Reducing time delays in selecting optimal Tx

11. • Patients are more motivated if they know (ex-ante) the
intervention is likely to work
• Issue of non-responders who might experience disutility (they
can feel “left-behind”)
Example: PreDx Diabetes Risk test
• Test estimates the patient’s risk for developing Type 2 diabetes
over the next five years
• This can further encourage patients to follow a healthy lifestyle
and take other preventive measures.
3. Increasing adherence or willingness to under-
take Tx or other interventions

12. A biomarker or other genetic characteristic allowing for
patient stratification can:
1. “Rescue” Tx that otherwise may either not have been licensed or
have been withdrawn
2. Increase the chance of a Tx meeting reimbursement criteria (if
targeting responders improves cost -effectiveness)
3. Accelerate R&D process for Tx (if stratification ascertained at an
early development stage)
4. Enabling Tx effective in a small fraction to be
made available

13. • Gefitinib for non-small-cell lung cancer (NSCLC) initially licensed, but
withdrawn when Phase III failed to show a survival benefit. With the
identification of EGFR mutations and its association with response rate to
TKIs, gefitinib was approved in the EU and other markets in combination with
the EGFR mutation test.
• NICE recommended trastuzumab for advanced and early-stage breast cancer
in HER2/neu positive patients identified with HER2/neu test. The Dx-Tx cost
per QALY was found to be below the standard threshold.
• Crizotinib targets a small subset of NSCLC patients with an ALK-positive
molecular abnormality. The development of the ALK FISH test has
accelerated the development process and increased the likelihood of
crizotinib delivering health benefits and commercial value.
4. Enabling Tx effective in a small fraction to be
made available – Examples

14. • Uncertainty around expected health effects and costs; influences the risk of poor
value for money for payers
• Value of information to patients about their medical condition independent of the
health outcome (Ash, et al, 1990)
• “Empowerment” (Payne, et al, 2012)
• Effect of reassurance (measured with EQ-5D?) (Kenen, 1996)
• Lifestyle choices and planning (Lee, et al, 2010)
• Example of Oncotype DX ® and MammaPrint ®
• Multi-gene assays estimating the risk of recurrence in breast cancer patients
following surgery
• Can guide intervention decisions and reduce the risk of dispensing unnecessary
chemotherapy (reduce resource costs to the healthcare system and adverse effect
for the patient)
5. Reducing uncertainty about value

15. • Low accuracy of Dx will decrease potential net gains to
patients and healthcare system
• False positive and false negative patients will not get most appropriate
therapy
• Tx can be more cost effective when used on its own
• When Dx does not provide binary response, depending on the size of
the subset for which the Dx does not provide clear-cut result and the
Dx cost relative to Tx
• When Dx has low accuracy
Other factors affecting value of
co-dependent technologies

16. • Barriers to evidence generation
• Cost and feasibility of certain study designs
• Protection of intellectual property rights of Dx
• Regulatory processes for diagnostics
• Assessment of competitive tests with similar clinical use
How prove value of co-dependent technologies?

17. How is value aggregated? Key issues Key merits
Net benefit As the sum of the benefits,
each assessed in monetary
terms
Challenges estimating the value in
monetary terms of each type of value
Allocating a monetary value to health has
been always one of the mayor criticisms
Arguably, a better grounding in economic theory
Facilitates the comparison of value and value for money across health
and other sectors
Use of monetary value may resonate better with some (private)
payers
MCDA As the sum of the points
assigned to each aspect of
value
The cost -effectiveness threshold would
need to be re-assessed in terms of the
cost per incremental “point”
A pragmatic approach, widely used in the UK public sector.
A more transparent (compared to a weighted QALY, or deliberative
process alone) means of addressing multiple criteria
MCDA is used in local NHS commissioning – potential to develop a
consistent priority-setting framework for both new and existing
health care technologies
Weighted
adjusted
QALYs
1. By QALYs gained, up-rated
or down-rated by one or
multiple weights to represent
the magnitudes of other
aspects of value; or
2. Direct estimation of how
people trade off QALY gains
with other value elements
Assumes that all other sources of value
are proportional to the number of QALYs
gained.
Implications for the threshold. If the value
of new technologies is assessed in terms
of a range of criteria, then opportunity
cost also must be considered in the same
terms, not just QALYs foregone. Even if a
simple social weighting or QALYs is
applied, opportunity cost will change
Is it relevant to state here the classic arguments in favour of the QALY
such as:
- Allows for comparisons across therapeutic areas in the NHS
- “A QALY is a QALY” argument
- Well established in the UK within HTA bodies (and academic
centres)
- Understood by health economics community
Deliberative
process
Weights are assigned by a
committee to each relevant
aspect of value
The weights are often implicit
Are implications for the threshold
Provides an element of flexibility
Is a well-recognised approach taken by HTA bodies around the world.
How aggregate value dimensions?
Source: adapted from Sussex, et al, 2013

18. • A joint Dx-Tx review of “at launch” technologies; to be done by a drug
committee to exploit synergies across Dx and Tx
• However, there is a need to address the lack of expertise of most drug
committees in the Dx area
• A separate Dx committee to develop Dx-specific expertise and to assess
multiple tests with similar clinical use
• However, there may be a trade-off if there are not enough decisions to justify
a distinct committee
• A comprehensive and consistent approach to assessing value of both Dx
and Tx
Proposed institutional processes for
co-dependent technologies

19. Proposed institutional processes
New Dx
Dx linked to a Tx
(companion Dx)
Dx-Tx pair
launched
simultaneously
Dx-Tx joint
assessment via
Drug process
Single Dx
launched
separately
Dx assessed via
Diagnostic-
dedicated process
Multiple Dx with
same clinical use
Dx assessed via
Diagnostic-
dedicated process
Dx not linked to a
Tx
Dx assessed via
Diagnostic-
dedicated process

20. • Until recently, Dx and associated Tx assessed via different
committees (MSAC and PBAC)
• No clear structure for consideration of the interactions and benefits
from joint use
• New coordinated process and decision framework for “co-
dependent technologies”
• “Integrated” applications combining information from Dx and Tx
manufacturers
• Reimbursement decisions are made jointly by PBAC and MSAC to
ensure optimal clinical use (Merlin, et al, 2012)
• The preferred type of evidence to show clinical benefit is a randomised
clinical trial
International experience: Australia

21. • NICE has dedicated-process for stand-alone Dx that follows
very closely that used for drugs
• Strong preference for measuring health gains with the QALY
• Value dimensions beyond health effects, such as value of information
to patients and process-related benefits, are not explicitly factored in
• “At launch” combinations are appraised via the drug review
programme (TAs)
• No explicit consideration of test-related parameters (accuracy, costs)
• Value dimensions beyond health effects are not explicitly factored in
International experience: NICE in England
and Wales

22. • The use of Dx-Tx combinations can deliver health gains and cost savings within the
health care system, but also generate broader benefits to patients and society
• To ensure efficient use of limited resources, health decision makers should take
account of the full value generated by health technologies
• Clear incentives are needed to encourage evidence collection
• HTA and other decision making systems need coordinated and consistent approach
to assessing value of Dx and Tx
• NICE is heading in this direction, but does not yet have a comprehensive approach
to assessing the value of Dx or Tx
• In Australia, the common methodology needs to be supported by a realistic view of
evidence development
Conclusions

23. Ash, D.A., Patton, J.P. and Hershey, J.C. (1990) Knowing for the sake of knowing: The value of prognostic information. Medical
Decision Making. 10(1), 47-57.
Garau, M., Towse, A., Garrison, L., Housman, L. and Ossa, D. (2012) Can and should value based pricing be applied to molecular
diagnostics? Personalized Medicine. 10(1), 61-72.
Garrison, L.P. and Austin, M.J.F. (2007) The economics of personalized medicine: A model of incentives for value creation and
capture. Drug Information Journal. 41(1), 501-509.
Lee, D.W., Neumann, P.J. and Rizzo, J.A. (2010) Understanding the medical and nonmedical value of diagnostics testing. Value in
Health. 13(2), 310-314.
Merlin, T., Farah, C., Schubert, C., Mitchell, A., Hiller, J.E. and Ryan, P. (2012) Assessing personalized medicines in Australia: A
national framework for reviewing codependent technologies. Medical Decision Making. 33(3), 333-342.
Kenen, R.H. (1996) The at-risk health status and technology: A diagnostic invitation and the gift of knowing. Social Science &
Medicine. 42(11), 1545-1553.
Payne, K., McAllister, M. and Davies L. (2012) Valuing the economic benefits of complex interventions: When maximising health is
not sufficient. Health Economics. 22(3), 258-271.
Sussex, J., Towse, A. and Devlin, N. (2013) Operationalising value based pricing of medicines: A taxonomy of approaches.
Pharmacoeconomics. 13(1), 1-10.
References

24. To enquire about additional information and analyses, please contact
Martina Garau: mgarau@ohe.org
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