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© Debiopharm Group 2014
ISPOR 19th International Meeting
May 31st – June 4th 2014, MontréalInternational Society for Pharmacoeconomics and Outcomes Research
Pascale Boyer Barresi, CFABusiness Analysis, BD&L
September 2014
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Topics
• Pharmaceutical Pricing• Risk Sharing & Performance-Based Agreements• Use of Big Data & Healthcare Analytics• Changing the Drug Development Paradigm• European HTA Collaboration • Health Economics in the Context of Personalized Medicine• Valuing Targeted Therapies• Market Access Modelization• Value-based Pricing across Indications
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Pharmaceutical Pricing
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Pharmaceutical Pricing
• Research Process
• Pricing Choices• Premium: class (re)-definition = higher than current competition =
unique & superior value proposition; sufficiently higher to benoticeable
• Parity: similar to current competition (tactically slightly higher or lower)
• Penetration: discount = within reach of current competition, sufficiently lower to be noticeable OR low-ball = far lower thancompetition, implying a value shift (generics)
Preclinical -36 months -24 months -12 months Launch Post Launch
Secondary research: establish ranges, hypotheses and monitor the market
Qualitative research: narrow range, understanddynamics of decision making and fine tune
Quantitative research: specify the role and importance of price, enumerate trade-offs
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Pharmaceutical Pricing
• What type of price?• Public price (for cash payers)• Negotiated price (reimbursed price)• Hospital price• Ex-factory price (without discounts)• Net selling price (ExFact with discounts)
• Adjust your mindset and perspective to who you are talking to.Ex: for sales forecasts, you will use the net selling price. For pharmacoeconomic analysis, you will use the reimbursed price.
• Pricing terminology is very often country specific. Price has to beunderstood properly between affiliate and headquarters.
Where we stand to forecastour revenue base
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Pharmaceutical Pricing
• Focus
• The best use of pharmacoeconomics is to determine the value of medications in use. This will help you define where you will generatemost of the value.
• Pharmacoeconomics should:• Drive clinical research protocols• Describe important aspects of the market• Inform and guide pricing• Help customers comprehend value and use products efficiently &
effectively.
Player Will focus on:Clinicians Clinical effectsPayers Cost (total budget) & HTA Patients Out of pocket cost & effectsPharma Science
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Pharmaceutical Pricing
• Drivers in determining price vary based on the disease state and therapy offered
Public Policy Environment
CompetitiveEnvironment
ReimbursementEnvironment
Patient & Disease
Characteristics
Decision Making
Value
Product Strategy
CompanyNeeds & Abilities
Price
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Pharmaceutical Pricing
• Valuation and pricing activities
EarlyDevelopment•Financial model of disorder(s)
• Identification of leverage points & critical successfactors
Preclinical& Phase I• Identification of differentialvalue, initial pricingestimates, scenarii & analysis
Phase II & III•Measurementin trials (endpoints, comparators,…)
•Develop & refine pricingscenarii
Pre-launch•Refine and test pricelevels, scenarii & value proposition
•Final pricingstrategy (list & tactical) decisions
Launch•Communi-cation of value
Post-launch•Ongoingpricemanage-ment
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Pharmaceutical Pricing
• Pricing and reimbursement are linked together.• Decisions are based on perceived value.• Value perceptions are based on both qualitative and quantitative
factors.• Decision making processes are influenced by the context.
• Key questions:• Who decides about the price of a pharmaceutical product? How?
Manufacturers, health authorities, formal evaluation, contracting,…
• Who decides about the listing and reimbursement? How?National and/or local health authorities, formal evaluation, contracting,…
• Who decides about the utilization? How?Individual physicians, professional guidelines, national and/or local health authorities, evidence-based, patients’preferences,…
• Who pays the price? How?Private payers, public payers, patients, insurance premiums, co-payments, fixedbudgets,…
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Pharmaceutical Pricing
Pricing takesplace beforelaunch in somecountries and after launchin others.
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Pharmaceutical Pricing
• Pricing Strategy Process
Business Strategy•Pricing & ReimbursementStrategy
•Payer/Managed MarketsStrategy
Build & Execute Plan•Identify options to explore•Perform gap analysis•Ensure alignment
Model & Test Strategies•Design & test programs•Ensure «real world» evaluations
•Analyze & offer refinements
ImplementationDecisions•Strategies & programs•Tactics & targets•Metrics for evaluation•Educate for execution
Implementation•Execute consumer communication
•Evaluate progress vs. metrics•Refine strategies & tactics
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Risk-Sharing & Performance-BasedAgreements
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Performance-Based & Risk Sharing Agreements
• Pay for performance• Old concept…
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Performance-Based & Risk Sharing Agreements
• Basics in health economics:• Drugs are approved, launched & reimbursed under conditions of
uncertainty affecting:• Efficacy• Effectiveness (real world)• Risk• Models (links between surrogate markers & long term
outcomes)• Cost effectiveness• Budget impact (affordability)
• Gathering more evidence is costly
BiologyImprovement in comorbidities•Glucose•Cholesterol
ClinicalImproved clinicaloutcomes•Cardiovascular•Cerebrovascular
EconomicsBetter healthoutcomes•Length of life•Quality of life
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Performance-Based & Risk Sharing Agreements
• Key Characteristics of PBRSA:1. Program of data collection agreed between the payer and the
manufacturer2. Data collection is starting right after the regulatory approval3. Pricing, reimbursement & revenue for the product are linked to
the outcome of this data collection4. The goal of the data collection is to address uncertainty (efficacy
in a specific population, efficacy in a broader population,…)5. The distribution of risk is different between the payer and the
manufacturer than the historical relationship• Understanding the real outcomes could help predict long-term
adoption and impact
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Performance-Based & Risk Sharing Agreements
• Payer response to increasing cost pressures:• Increasing patient co-payment• Pre-use authorization• Quantity and dose limitation• Benefit restrictions• Denial of coverage• Performance-based & risk sharing agreements
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Performance-Based & Risk Sharing Agreements
• What are the options of the payer?
Payer options
YesPayer adopts: no
new evidencerequired
NoPayer refuses to
adopt
Yes butPayer adopts with
additional evidence(CED)
Manufacturer has the option to
reapply withmore
evidence
Use only in research
CED (Coverage
with Evidence Development)
withrenegotiation.
No pre-specified
agreement
CED linked to performance agreement
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Performance-Based & Risk Sharing Agreements
• PBRSA Taxonomies… several references but key dimensions
Non-outcome based Outcome based
Patient Level
• Utilization capitations• Discounted treatment
initiation• Fixed cost per patient
• Price linked to per patient outcome
• Conditional treatmentcontinuation
• Money-back guaranteePopulation Level
• Market share• Price-volume• Expenditure/budget cap• Price change/discount
• Only with research(coverage with evidencedevelopment) usingobservational study or RCT
• Only in research(patients only getaccess if they agree to participate in a study)
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Performance-Based & Risk Sharing Agreements
• Practical difficulties:• Transaction costs
• Development of processes• Health personnel time to administer the scheme• Limitation of current medical information systems to measure
and track performance• EMR are sometimes not implemented in all countries
• Operational• Agreeing on the scheme details• Identification of eligible patients and relevant clinical outcomes
and/or therapeutic goals• Measurement errors with validating and confirming clinical end
points (scales for clinical measurement)• How are competitor products treated?
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Performance-Based & Risk Sharing Agreements
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12 11
42 2 1 0 0 0 0 01 0 1 1
14
0 02 1 1 1 1 00 0
57
25
0 13
1 1
4 31
0
5
10
15
20
25
30
35
40
45
50
Number of Active Performance-based & Risk-sharing Agreements by country (2014)
Coverage with Evidence Development Conditional Treatment Continuation Performance-Linked Reimbursement
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
• Traditional methods vs. Machine-learning methods to extractthe best from Big Data
Traditional methods Machine-learning methods
Good methods for developing well-matched control groups but no magic bullets
Many methods with the same basic approach
These methods control only for observables but do not for endogeneity or confounding.Human intervention can biased classification.These methods can be used for small datasets.
Basic approach: Use learning datasets to develop highly accurate classification algorithm.• Apply algorithm to another dataset to predict classification.• Rules should be as simple as possible while maintaining accuracy.
– Should be able to classify data without human intervention– Should be efficient with very large datasets
Some machine learning methods use regression methods with a penalty term to adjust for the danger of overfitting(spurious correlations).
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
• Summary• Rapid expansion in data (volume, velocity, and variety)• Machine learning approaches focus on prediction but some can
also be used to estimate treatment effects• Machine learning methods offer opportunities for speed to answer
but traditional challenges with observational data do not go away• More data doesn’t help with bias problems unless it helps with
control variables through data linkage• For treatment effect estimation still need to think about possible
sources of bias and their implications for methodology and data used for model building
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
Definethe
question
Design the
study
Preparethe data
Analyze
Interpretthe
results
Informclinicalpractice
The outcomeresearch process
• Exploratory vs. confirmatory• Grounded in clinical
practice, policy• Builds on current knowledge• Guided by data
• Fragmentation of health care (and data)
• Completeness and accuracy of data
• Standardization of data across organizations
• Understand the workflow that generated the data
• Identify and address data quality problems –Missingness –Inconsistencies
• Engage data partners and experts
• Data may not reflect a random sample of patients
• Reference standards for prediction may be inadequate
• Measurement of concepts, coding changes over time
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
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Use of Big Data & Healthcare Analytics
Health Plan Claims
InpatientChart
Information
OutpatientChart
Information
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Big Data – Barriers & Opportunities
ADJUSTEvidence influences continual
improvement
DESIGNClinicians and
researchers design care based on
evidence
IMPLEMENTResearchers
collect data frompilot and control
settings
EVALUATEData show whatworks and what
doesn’t
In a learninghealthcare system, research influences practice and practice influences research.
Innovation isdisseminated to improve care throughout the system
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Changing the Drug DevelopmentParadigm
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Changing the Drug Development Paradigm
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Changing the Drug Development Paradigm
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Changing the Drug Development Paradigm
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Changing the Drug Development Paradigm
• What are the key points of this new paradigm?• Greater acceptability of enrichment designs and surrogate
endpoints for regulatory approval• Patient-powered research networks and country-sponsored
registries• Selected pockets of healthcare systems and some countries with
reliable mechanisms to track patients healthcare use acrosssettings of care and longitudinally through clinically-rich electronichealth records
• Greater harmonization between regulatory agencies and HTA bodies in Europe (we will talk about that in the next section)
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European HTA Collaboration
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European HTA collaboration
ICT: Information and Communication Technology
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European HTA collaboration
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European HTA collaboration
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European HTA collaboration
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Health Economics in the Context of PersonalizedMedicine
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Health Eco & Personalized Medicine
• Personalized Medicine• A form of medicine that uses information about a person’s genes,
proteins, and environment to prevent, diagnose, and treat disease, or to make a prognosis
• Also called precision medicine
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Health Eco & Personalized Medicine
• Key Policy Issues• Evidence gaps
• Most tests come to market without FDA review/approval• Variable use in practice
• Tests diffuse into practice unevenly• Many patients who could benefit are not offered the test• “Off-indication” use is common
• Uneven and uncertain payment structure • Test-specific billing codes are often unavailable • Insurer policies towards reimbursement can vary widely
• Lack of evidence of cost-effectiveness • Models, when they exist, often rely on registration data even
though real world use may be very different from ideal use.
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Health Eco & Personalized Medicine
Pro Against
Evidence-based personalizedmedicine: is PM a “micro” version of EBM? What evidence should payers require?
Traditional health economic approaches to framing and assessing PM have had limited success addressing specific issues
Payer challenges in evaluating evidence: small patient numbers, new methodologies (WGS), pricingand affordability, how much can we generalize results? How to demonstrate clinical utility?
Uncertainty in economic analyses of PM applications precludes effective use by policymakersInherent value of PM information to patients – personal utility – is not explicitly evaluated
AMCP criteria: analytical validity, clinical validity, clinical utility, costeffectiveness (see next slide)
Influence of PM attributes on uptake – and thus population impact – is not captured
CEA is relevant and important Efficiency is achieved underspecific conditions (see nextslide)
CEA too slow
WGS: Whole Genome SequencingAMCP: Academy of Managed Care Pharmacy
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Health Eco & Personalized Medicine
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Health Eco & Personalized Medicine
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Health Eco & Personalized Medicine
• CEA is relevant and important • Not all biomarker tests are worth doing • Need to manage efficient use of this technology • Answers are not always straightforward
• Efficiency is achieved when: • The drug is narrowly targeted (benefits only a small subset of
potential patients) • Test results are actionable to providers and patients • Testing substantially improves clinical outcomes • Drug cost is large compared to diagnostic cost
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Valuing TargetedTherapies
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Valuing Targeted Therapies
Getting the question right (1/2)• How is test used in clinical practice?
• Diagnostic or screening • Triage, replacement, add-on• Cut-point used• If combined with another test, how is this done (eg IHC 2+ & FISH
+ve = positive?) • Does the test result actually change patient management? • How will the test result be acted upon?
• Positives • Negatives• Equivocals• Unreadables
• Could vary considerably between countries (& within countries!)
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Valuing Targeted Therapies
Getting the question right (2/2)• Who uses the test and where?
• Home, GP, Spec, Emergency Dept• Near-patient or lab
• Who gets the test? • The symptomatic patient only? • Does it then prompt screening of others +siblings, +contacts
• Who benefits from the test? • In the broadest sense
• What are the health sector implications? • What are the insurance implications? To the patient (and relatives!) • Is this test result of value to others? Eg. other targeted therapies • Is other information gained at the same time?
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Valuing Targeted Therapies
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Valuing Targeted Therapies
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Valuing Targeted Therapies
Important and unanswered questions about the ‘real world’ use of targeted therapies cannot be answered with hypothetical cohort simulations informed primarily by data from trials: - Which patients get tested and treated? - How accurate is testing in the clinical setting? - What testing and treatment approaches are used to direct targeted therapy in actual clinical practice?
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Valuing Targeted Therapies
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Valuing Targeted Therapies
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Valuing Targeted Therapies
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Valuing Targeted Therapies
GEP: Gene Expression Profiling
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Market Access Modelization
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Market Access Modelization
• Context: MCDM/MCDA: Multi-Criteria Decision Making/analysis@ Roche for Market access & HTA modelization
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Market Access Modelization
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Market Access Modelization
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Market Access Modelization
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Market Access Modelization
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Market Access Modelization
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Value-Based PricingAcross Indications
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VBP across Indications
• Company Perspective
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VBP across Indications
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VBP across Indications
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VBP across Indications
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VBP across Indications
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VBP across Indications
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VBP across Indications
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VBP across Indications
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VBP across Indications
• HTA Perspective (Italy)
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VBP across Indications
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VBP across Indications
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VBP across Indications
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Conclusion
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Conclusion
• Pharmaceutical Pricing• To be understood and implemented early stage
• Risk Sharing & Performance-Based Agreements• A solution for giving access to patients but with administrative and
operational burden• Use of Big Data & Healthcare Analytics
• A key trend to follow for better efficacy in care and drug development• Changing the Drug Development Paradigm
• A new paradigm, still to be accepted by regulatory authorities• European HTA Collaboration
• On the right track. It will bring a lot of value even if healthcare budgets are still managed country by country.
• Health Economics in the Context of Personalized Medicine & Valuing TargetedTherapies
• Health economics is useful in a pre-determined context to help access• Market Access Modelization & Value-based Pricing across Indications
• Key in order to better anticipate the market reality
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