Value Assessment: Building Payercentric value propositions to inform
decision-making
Aris Angelis and Panos Kanavos
Medical Technology Research Group, LSE Health
Advance-HTA dissemination workshop, Santiago, 9 September 2015
Acknowledgements
This presentation has been prepared in the context of
Advance-HTA, which has received funding from the
European Commission, DG Research.
http://www.advance-hta.eu/
Outline
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Objective
What is a Value Proposition?
How can payers/insurers compile and use it?
What evidence is required and what are the
caveats?
Case study of an oncology indication
Conclusions
Objective
• Payers and HTA agencies need to be convinced about
clinical- and cost-effectiveness, i.e. if the new therapy
represents good value for money.
• To outline and discuss the range of criteria that can
inform coverage decisions both when HTA is used as
the primary criterion and when HTA is one of the criteria
used in decision-making
Payer-centric Value Proposition
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In negotiating with manufacturers, payers need to compile a
detailed understanding of the value and implications of a new
technology
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This can be achieved through a patient-centric value proposition
A strong payer-centric Value Proposition will contain
compelling evidence that
– Showcases all traditional dimensions of value (therapeutic, safety and
economic benefits)
– Translates the clinical profile into a compelling cost/effectiveness ratio
– Investigates the full impact of the innovation to payers and HTAs
– Uses comprehensive and good quality evidence
– Leverages critical value dimensions with a view to constructing tailormade market access plans
Criteria clusters to aid coverage
decisions
• High quality evidence is needed in order to minimise
uncertainty
• The broad criteria clusters to aid coverage decisions should
have the following evidence structure:
• Burden of Disease (severity, unmet clinical need)
• Therapeutic benefit (including primary and secondary
endpoints from pivotal trials)
• Safety and tolerability (including adverse events and
toxicities)
• Product innovation (including patient convenience, MoA,
likely spillover effects)
• (Socio-) Economic and budget impact (including any
economic evaluation data)
Internal validity
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Internal validity refers to the extent at which an
observed effect can be attributed to the intervention
under investigation
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Internal validity depends on the study’s design, conduct
and analysis and main features include those of
appropriate samples, sufficient follow-ups and suitable
outcome measures
 Methodological flaws can increase the risk of bias,
leading to divergence from the true treatment effect
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Blinding, random allocation and its concealment are
recognised as keystones of internal validity, known to
reduce risk of bias
External validity
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The issue of external validity of evidence is mainly
caused due to the issue of generalisability that arises
because of the gap between efficacy (RCTs) and
effectiveness (pragmatic trials) data, i.e. ideal
conditions vs. routine clinical practice
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External validity is primarily determined by the study
design and conduct
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It is affected by multiple factors of which one of the
most important is patient characteristics; other factors
include choice of outcomes/endpoints used and
adverse events assessed, drug regimen and mode of
administration, patient compliance, study duration,
sample sizes, costs etc.
Addressing evidence validity
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If significant internal and external validity considerations
are evident, suggesting that the true treatment effect
may be not aligned with the one supported by the
manufacturer, and/or that it cannot be observed in real
clinical settings, the regulator may request additional
data and studies:
(a) of an amended design that correct such
methodological flaws to reveal “corrected” effects
(b) possibly involving updated patient inclusion and
exclusion criteria, more relevant outcome
measures, or longer follow-up period to reveal
more realistic effects.
Payer preferences
• Critical elements of evidence that payers may
require include:
– Relevant comparator (head-to-head)
– Long time horizon
– Real-life conditions
– Statistical superiority
Payer preferences –
Relevant comparator (head-to-head)
Relevant comparator (head-to-head)
– Although superiority may be shown against placebo or
BSC on all primary- and secondary endpoints, it may deter
payers from considering the improvement as being major.
– Payers prefer to see head to head (H2H) trials where the
technology under consideration is compared against
another active treatment. In the absence of H2H data,
payers would need to see data for detailed indirect
comparison across the primary and secondary endpoints
that incorporate any difference in trials.
Payer preferences – time horizon,
real life data, superiority
– Time horizon: If it is not long enough to capture full
clinical- and economic impact of disease payers may
demand long-term (real life) data possibly as part of postmarketing authorization studies involving the use of
registries and continuing data collection
– Real-life conditions: Clinical- and cost effectiveness data in
real-world vs. a clinical setting
– Statistical superiority: E.g. for the case of acute disease, a
non-significant claim on overall survival will negatively
impact the value perception for payers. Without a clear
statistical superiority claim for overall survival, payers will
consider new therapies at most comparable to existing
ones despite new therapies showing considerable
additional benefits on other attributes
Value Proposition – In a nutshell
• The Value Proposition (VP) for a new medical technology
essentially consists of a series of value statements relating to its
(superior) performance in the context of a particular disease
indication, being accompanied by the necessary evidence to
support them.
• The value statements span the widest possible range of value
drivers for which the new technology is superior, or at least
comparable, to the relevant comparators of interest.
• Main value drivers include therapeutic benefit, safety and
tolerability, product innovation level and efficiency (i.e. value for
money) value dimensions.
Building a Value Proposition –
Compiling the available evidence
• A systematic literature review should be undertaken,
with a fully transparent study protocol detailing the
research questions and the actual variables of interest.
• For example, for the case of clinical evidence
(therapeutic and safety) the relevant patient population,
interventions comparators, outcomes and study designs
to be analysed should be ideally specified.
• Methodological details should be supplied on how the
evidence was identified, selected, extracted but also
possibly synthesised, for the case that an indirect
comparison approach needs to be adopted.
Building a Value Proposition –
Compiling the available evidence
Evidence identification
• The evidence sources searched, including any electronic
databases, peer review journals, conference proceedings etc., but
also the precise search strategy used, including the different
combinations of keywords.
Evidence selection
• The explicit inclusion and exclusion (i.e. eligibility) criteria directing
how to accept or reject the identified evidence.
Evidence extraction
• The methodological process adopted by the reviewers to extract the
selected evidence, including any existing extraction templates, their
number, their disagreement resolving approach, and any quality
assessment procedure adopted.
Building a Value Proposition –
Oncology indication example
Value Proposition Overall Statement
“New Drug is the oral option for treating metastatic colorectal cancer in adults that
delivers comparatively superior medical/therapeutic and economic benefits with fewer
safety and tolerability trade-offs”
Superior Value Profile relative to its competitor(s)
Relative to current standard of care in colorectal cancer, New Drug should be considered
as the “product-of-choice” because it
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Is a First-in-class for advanced colorectal cancer treatment
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Offers the best and most effective standard of care with fewer safety and
tolerability trade-offs
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Provides the best improvement in patient reported outcomes
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Offers a superior usefulness profile across all available treatment options and
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Represents the best value-for-money among existing alternatives
Ultimately
Based on the unique value profile, New Drug’s placement in the treatment of advance
colorectal cancer should be first line, before all the other currently available options
Value Drivers - Therapeutic Evidence
Key messages
Supporting evidence
Primary endpoint: Overall Survival (vs. BSC)
NEW DRUG prolongs survival by a median of 4.8 months
vs. BSC
• OS, (median) Months (95% CI) = 15.5 (14.3 - not reached) vs. 10.1
(6.9 - 12.3)
Full population
NEW DRUG reduces the risk of death by 37 % vs. BSC
• HR for death (95% CI) = 0.73 (0.60 - 0.85)
Steroid population
NEW DRUG reduces the risk of death by 30 % vs. BSC
• HR for death (95% CI) = 0.60 (0.46 - 0.78)
Non-steroid population
NEW DRUG reduces the risk of death by 51 % vs. BSC
• HR for death (95% CI) = 0.39 (0.27 - 0.55)
Primary endpoint: Overall Survival (vs. OLD DRUG)
In indirect comparison between NEW DRUG and OLD
DRUG using the Bucher method:
Full population
There is no significant difference in median overall survival
(OS)
• HR (95% CI) = 0.905 (0.780 - 1.116)
Steroid population
There is no significant difference, NEW DRUG is noninferior to OLD DRUG
• HR (95% CI) = 0.955 (0.786 - 1.215)
Non-steroid population
There is significant difference, NEW DRUG is superior to
OLD DRUG
• HR (95% CI) = 0.773 (0.587 – 0.928)
By extrapolating the individual survival curves using
Weibull parametric fits and correcting for a different
placebo effect, NEW DRUG prolongs statistically survival
vs. OLD DRUG by a mean of 2.15 months
• Months (95% CI) = +3.10 (1.74 - 4.23)
Value Drivers - Therapeutic Evidence
Evidence
NEW DRUG
Evidence
BSC
Level of significance
• 7.1 (6.2 - 8.4)
• n/a
• n/a
• 1.9 (1.1 - 2.6)
• n/a
• n/a
• HR = 0.45 (0.30 - 0.54)
• HR = 0.65 (0.54 - 0.78)
• HR = 0.31 (0.21 - 0.42)
Full population
Steroid population
Non-steroid population
• 6.8
• n/a
• n/a
• 1.7
• n/a
• n/a
• HR = 0.51 (0.45, 0.58)
• HR = 0.62 (0.52 - 0.73)
• HR = 0.40 (0.34 - 0.48)
• Time to first SRE (TTF SRE); (median)
mo.
• 15.6 (13.4 - 18.8)
• 12.4 (8.9 - NR)
• HR = 0.59 (0.47 - 0.70)
• Biomarker response (≥50% from
baseline); % of patients
• 51.0%
• 2.5%
• OR = 81.41 (42.2 - 175.0)
• Biomarker progression; (median) mo.
• 7.3 (5.1 - 8.9)
• 2.0 (1.6 – 3.3)
• HR = 0.35 (0.22 - 0.43)
• Risk of biomarker progression
• -65%
• Objective radiographic response (ORR)
• 26.9%
• 2.8%
• OR = 10.2 (4.87 - 21.2)
Key messages
Secondary endpoints (vs. BSC)
NEW DRUG shows superiority vs. BSC
in all secondary endpoints of disease
progression including:
• Radiographic progression free survival
(rPFS); (median) mo.
Full population
Steroid population
Non-steroid population
• Modified progression free survival
(mPFS); (median) mo.
Value Drivers - Therapeutic Evidence
Evidence
NEW DRUG
Evidence
OLD DRUG
Level of significance
Full population
Steroids population
Non-steroids population
• 7.1
• n/a
• n/a
• 5.1
• n/a
• n/a
• HR = 0.66 (0.55 - 0.80)
• HR = 0.95 (0.76 – 1.25)
• HR = 0.50 (0.35 - 0.57)
• Modified progression free survival
(mPFS); (median) mo.
• 6.8
• n/a
• HR = 0.78 (0.65 - 0.91)
• TTF SRE mo.
• 15.6
• 24.0
• HR = 1.18 (0.85 - 1.44)
• TTF SRE incidence; % of patients
• 30.9%
• 23.6%
• OR = 1.05 [0.81; 1.40]
• Biomarker response (≥50% from
baseline); % of patients
• 51.0%
• 35.0%
• OR = 11.57 (5.3 - 32.2)
• Objective radiographic response (ORR)
• 26.9%
• 15.8%
• OR = 2.54 (0.67 - 7.22)
Key messages
Secondary endpoints (vs. OLD DRUG)
NEW DRUG shows superiority or
comparability vs. OLD DRUG in several
secondary endpoints of disease progression
including:
• Radiographic progression free survival
(rPFS); (median) mo.
Value Drivers - HRQOL Evidence
Evidence
NEW DRUG
Evidence
BSC
Level of significance
• NEW DRUG is associated with a delay
in deterioration of QoL by 6.5 months vs.
BSC
10.6 (7.4-11.99)
months
4.1 (2.21 – 5.74)
months
HR= 0.55 (95%CI [0.47, 0.68];
P<0.001
• The treatment effect favoured NEW
DRUG vs. BSC at all visits between
Week 4 and Week 25
No significant changes
from baseline until
Week 25
Significant decrease
(worsening) from
baseline at Week 4, 8,
16 and 25
Key messages
Evidence
NEW DRUG
Evidence
OLD DRUG
Key messages
Patient reported outcomes (vs. BSC)
Patient reported outcomes (vs. OLD
DRUG)
Difficult to make a robust claim currently as
there are no comparable endpoints
Level of significance
Value Drivers - Safety Evidence
Evidence
NEW DRUG
Evidence
BSC
• Any adverse events (AEs); % of patients
• 92.1%
• 91.3%
• Adverse drug reactions (ADRs), ie AEs
possibly, probably or definitely attributed
to the drug
• 64.5%
• 62.2%
• Serious AEs
• 30.1%
• 33.8%
Full population
Steroid population
Non-steroid population
• 8.61 (±0.18)
• n/a
• n/a
• 3.52 (±0.22)
• n/a
• n/a
• HR = 0.44 (0.35 - 0.49)
• HR = 0.59 (0.48 - 0.68)
• HR = 0.34 (0.29 - 0.39)
• AEs causing treatment discontinuation;
(% of patients)
• 7.1%
• 9.7%
• OR = 0.86 (0.50; 1.16)
• AEs leading to death
• 3.8%
• 3.2%
Key messages
Level of significance
Safety (vs. BSC)
NEW DRUG is well tolerated. Its safety
profile is comparable to BSC including
similar or improved safety in regards to:
• Time to treatment discontinuation
(TTTD); (mean) months
Value Drivers - Safety Evidence
Evidence
NEW DRUG
Evidence
OLD DRUG
Level of significance/
Justification
• Time to treatment discontinuation
(TTTD); (mean) months
• 8.61
• n/a
• HR = 0.75 (0.63 - 0.80)
• Treatment discontinuation due to an AE
(% of patients)
• 7.1%
• 14.2%
• OR: 1.114 [0.751; 1.788]
• Bone pain
• Higher likelihood
• Lower likelihood
• OR: 1.85 [1.28 - 2.66])
(p.s. concomitant corticosteroids are
Key messages
Safety (vs. OLD DRUG)
NEW DRUG has a tolerability and a safety
profile that is comparable to OLD DRUG,
including all specific AEs of all grades,
except for bone pain.
used in the case of OLD DRUG)
Importantly, there is a difference in toxic
effects, favouring NEW DRUG:
Hepatotoxicity (requiring regular liver
function testing)
• Absent
• Present
• Due to the different MoA
Value Drivers - Patient Convenience
Key messages
Supporting evidence
Usefulness profile (vs. BSC)
NEW DRUG can be taken orally, does not
have to go through the toxic IV chemo
process as part of BSC; allows patients to
be treated at home
• NEW DRUG are administered as capsules through an oral RoA compared to BSC
which is administered intravenously under hospital settings
NEW DRUG is more convenient to take
with limited special instructions for
administration compared to BSC
• NEW DRUG can be taken on full stomach, without the need for concomitant
corticosteroids compared to BSC
Usefulness profile (vs. OLD DRUG)
NEW DRUG is more convenient to take
with limited special instructions for
administration compared to OLD DRUG
• NEW DRUG can be taken on full stomach, without the need for concomitant
corticosteroids compared to OLD DRUG that needs to be administered on empty
stomach and in combination with corticosteroids
NEW DRUG is associated with less
monitoring requirements compared to OLD
DRUG
• NEW DRUG needs outpatient monitoring every 8 weeks compared to OLD DRUG
which needs every 4 weeks due to the risk of hepatotoxicity and increased blood
pressure associated with OLD DRUG
Value Drivers - Value For Money
Key messages
Supporting evidence
Value-for-money (vs. BSC)
NEW DRUG is the most cost effective
treatment options in advanced colorectal
cancer, using economic evaluation data
• At Base case
• From extensive scenario analyses and
deterministic and probabilistic sensitivity
analysis showing that the model is robust
NEW DRUG reduces the cost burden of
disease vs. BSC?
• The base case incremental cost effectiveness ratio (ICER) for NEW DRUG was £40,587
against BSC
• There is a 85% probability of being cost-effective against BSC at a WTP of £50,000 per
QALY gained, a threshold previously used for the recommendation of OLD DRUG
(Life extending, “end of life” treatment eligibility criteria include
i) treatment indication is for patients with less than 24 months life expectancy,
ii) treatment offers a life extension of at least 3 months, and
iii) treatment is licensed/indicated for small patient populations)
• Need to develop cost/minimization scenarios to the Payers in the study countries
Value-for-money (vs. OLD DRUG)
NEW DRUG is the most cost effective
treatment options in advanced prostate cancer,
using economic evaluation data
• At Base case
• From extensive scenario analyses and
deterministic and probabilistic sensitivity
analysis showing that the model is robust
• Using OS and rPFS data respectively:
NEW DRUG I reduces the cost burden of
disease vs. OLD DRUG
• The base case incremental cost effectiveness ratio (ICER) for NEW DRUG was £15,195
against OLD DRUG
• There is a 81% probability of NEW DRUG being cost-effective against OLD DRUG at a
willingness to pay (WTP) of £20,000 per QALY gained, and 99% at a WTP of £30,000 per
QALY gained
• NEW DRUG is associated with an ICER of £1,505 for each additional month of survival and
with an ICER of £1,251 for each additional month of progression free survival
• Assuming equal overall survival, time to treatment discontinuation and drug acquisition cost,
while including the difference in monitoring costs and requirement for steroids, there is a cost
saving of £1,050 while resulting in the same number of QALYs
Comparative presentation of value drivers
NEW DRUG vs. OLD DRUG
Inferior
Not stat. dif.
Superior
Value drivers
Efficacy
Overall Survival (OS)
Overall Survival (OS)
Skeletal Related Effects (SRE)
Skeletal Related Effects (SRE)
Objective Response Rate (ORR)
Radiographic Progression Free Survival (rPFS)
Modified Progression Free Survival (mPFS)
Biomarker Response
Time to treatment discontinuation (TTTD)
Radiographic Progression Free Survival (rPFS)
Median
Mean
Time to first
Incidence
Median time to
Median time to
Patients proportion
Patient proportion free of
progression
?
Biomarker Progression
Patient proportion free of
progression
Skeletal Related Effects (SRE)
Biomarker Progression
Safety
Adverse events
Tolerability
Adverse events
Innovation
Mechanism of Action (MoA)
Patient convenience
Socioeconomic
Direct medical costs
Value for money
Value for money
Rate
Median time to
?
?
Hypokalemia
Hepatotoxicity
Bone pain
?
Special instructions
Outpatient visits
Cost effectiveness
Cost minimisation
?
Key Takeaways for insurers and
HTAs
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Building a value proposition requires a range of
performance indicators across all critical value
dimensions of the drug
Additional data may be required to ensure satisfactory
internal and external validity
All stages of evidence collection, analysis, and
interpretation should be informed based on payer
preferences
Evidence should be identified, selected, extracted, and
synthesised using a fully transparent study protocol
A comparative presentation of value drivers should be
included in the summary of the results
Health Insurer’s Point of View
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Health Insurers may be willing to confirm the content of the
Value Proposition or build their own version reflecting their own
perspective.
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Based on this payers can judge whether a new therapy is
inferior, not statistically different or superior to a comparator
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Most data required should be available in the (peer reviewed)
literature
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If not published their credibility could be questioned
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Insurers may conduct their own (systematic) literature review(s) to ensure
all critical evidence is incorporated
Other sources may be required for the collection of real world
effectiveness data and resource use (cost of illness) data
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Registries and observational studies
Thank you!
Aris Angelis [email protected]
Panos Kanavos [email protected]
© Aris Angelis & Panos Kanavos, 2015