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Methods of Early Health Technology
Assessment in Precision Medicine
Janet Bouttell, Andrew Briggs and Neil Hawkins, Health Economics and Health Technology Assessment, Institute of Health and Wellbeing, University of Glasgow
Precision Medicine is a fast-moving environment. Many discoveries are in the pipeline and it is critical to find quick and effective ways of determining
priorities for research investment. Health economics has a role to play in this prioritisation by adapting the techniques of health technology
assessment to better serve the funders, investors and potential users of precision medicine ‘products’. Health Technology Assessment is commonly
used to generate cost-effectiveness analysis through methods including decision modelling. Given the multitude of products under development, the
majority of which will not succeed, HTA processes need to be fit for purpose – quick, with low evidence requirements and in themselves cost-effective.
This project aims to use the context of the Glasgow Molecular Pathology Node to assess and recommend methods for early HTA.
What is early Health Technology Assessment?
Traditional or ‘late’ HTA is undertaken when a technology is at a
late stage of development and information about its costs and
health impacts are generally known. It often involves highly
complex modelling which builds in uncertainty and involves a
significant input in time, resources and evidence requirements.
Early HTA is different as it includes methodology to address the
potential value of a technology when information about its cost
and effectiveness are not known and even the use of the
technology may not have been finally determined. Its aim is
provide quick, timely and useful information .
Table 1 summarises key differences between ‘early’ and ‘late’
HTA
Table 1: Contrasting early and late HTA
Early HTA
Characteristic
Late HTA
Conducted during the
development of the
technology
Timing
Peri and post approval
of technology
Aim to maximise
value of investment
/potential of
technology
Aim
Aim to maximise health
benefit with finite
resources
Prioritisation of
research
Choice of target
market profile
Informs
decisions
regarding
Need for and design
of studies
Comparative
effectiveness and costeffectiveness of
technology for different
indications/sub-groups
Case study One – Cardio-Vascular Disease
Measurement of multiple biomarkers on chip
device
In this project a multi-disciplinary research team is working to
develop a chip which can simultaneously measure a number of
separate bio-markers. The technology could potentially impact
across disease areas and in many different contexts.
The health economics approach has been to work with the
project team to develop a detailed conceptual model showing
the different potential applications in health and where value
could be generated either through improved quality of life,
extension of life or cost reductions in healthcare. Further
methods will be applied as the project progresses.
A conceptual model is often presented in the form of a
diagram and its principal purpose is to provide a means of
communicating understanding of a process within a group of
stakeholders. A conceptual model can be sufficient to inform a
no-go decision if stakeholders are not convinced of the
potential value of a technology.
A simplified conceptual model for the transcriptomic
signature is shown in Figure 3.
A potential conceptual model for the chip device is shown in
Figure 1 and illustrates a range of cost and health outcome
impacts which are dependent on both disease area and
setting.
Current pathway
Figure 1: Potential conceptual model for multi-biomarker
measurement chip
Need for further
research
Patient groups,
Pharma/Device
development
companies, research
funders
Stakeholders
Constrained
Resources
Committed
Fund programmes
where net present
value is greater than
zero (or the highest if
funds are limited)
Decision rule
Reimburse when net
health benefit is greater
than zero
Conceptual model
Generic disease
model
Headroom analysis
Value of information
analysis
Methods
Helps to inform an
evidence generation
plan
Evidence base fluid
Evidence
Typically single product
with limited number of
indications
One jurisdiction
Reimbursement
agencies, Patient
groups, Pharma/Device
development companies
Conceptual model
Bespoke decision model
Value of Information
analysis
Primary
Care
• Cheaper than
laboratory test
• Early diagnosis
so better health
outcomes
• Cheaper than
laboratory test
• Process
simplification
Secondary
care
• Early diagnosis
so better health
outcomes (e.g.
in Emergency
department)
• Bedside
monitoring
may improve
health
outcomes
Early HTA in the Glasgow Molecular Pathology
Node
The clinical research focus of the Glasgow node is in cancer,
cardio-vascular disease and inflammatory disease. The
following two case studies describe projects in cardio-vascular
disease and rheumatoid arthritis.
• Regular
monitoring
may improve
health
outcomes
Domestic
Remote
location
Specific to
indication/context
Evidence base fixed
CHRONIC
Scope
Figure 3: Simplified conceptual model for transcriptomic
signature in rheumatoid arthritis
Continue
People with
Rheumatoid
Arthritis who
have not
responded to
conventional
therapy
Test with Drug X
Stop
Potential pathway using transcriptomic signature for response to Drug A
Impact on costs and health outcomes
ACUTE
Potentially high
number of products
Indications to be
determined
Many jurisdictions
Figure 2: Potential impact of biomarkers in auto-immune
disease
• Early
diagnosis so
better health
outcomes
•
•
•
Cheaper
Simpler process
Early diagnosis
Case study Two – Transcriptomic signature of
treatment response in Rheumatoid Arthritis
This project developed from the ORBIT trial[1] and aims to
develop a panel and algorithm to predict treatment response to
biological treatments in Rheumatoid Arthritis (RA) – point A on
Figure 2. A conceptual model and headroom analysis are the
starting points for this health economic analysis but given the
importance of longer term outcomes in RA it may ultimately be
necessary to model more of the disease pathway.
People with
Rheumatoid
Arthritis who
have not
responded to
conventional
therapy
Positive
Treat with Drug X
Respond
Don't treat with
Drug X
Don't respond
Test for
markers of
response
to Drug X
Negative
The conceptual model above suggests that there may be cost
savings from not treating patients with a drug to which they will
not respond.
Headroom analysis builds on a conceptual model by bringing in
some quantitative evaluation[2]. Again the aim is to assess
whether it is worth proceeding with the investment. It uses
knowledge of costs, potential outcomes and the prevalence of
disease to determine the maximum ‘headroom’ available to the
developers to invest as well as the maximum charge for the test
which would be acceptable in a particular reimbursement
context.
Other early HTA approaches may ultimately prove useful in
assessing the value of similar projects in complex disease areas
such as rheumatoid arthritis.
Conclusions
Although some methodological work has been done on methods
of early Health Technology Assessment, little work has
specifically addressed the precision medicine environment.
Given the competing demands on health-care expenditure it is
vitally important that research in precision medicine is focused
on those areas where successful ‘products’ could make the most
difference and be the most cost-effective. This methodological
work aims to extend and refine the repertoire of methods
available to assess the cost-effectiveness of research projects at
an early stage and with minimal expense. The situation within
the Glasgow Molecular Pathology Node and the wider node
network will provide a rich context for this development work.
References:
[1} Porter, Duncan et al. "Tumour Necrosis Factor Inhibition Versus Rituximab For Patients With Rheumatoid Arthritis Who Require Biological Treatment (ORBIT): An Open-Label, Randomised Controlled, Non-Inferiority, Trial". The Lancet 388.10041 (2016): 239-247.
[2 Girling, Alan et al. "HEADROOM APPROACH TO DEVICE DEVELOPMENT: CURRENT AND FUTURE DIRECTIONS". International Journal of Technology Assessment in Health Care 31.05 (2015): 331-338.