ispor 19th international meeting - · pdf filepharmacoeconomics. should: • drive clinical...

© 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

2© Debiopharm Group 201417.04.2015Debiopharm International SA confidential2

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


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



• 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



• 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



• 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



• 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



• 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,…



Pricing takesplace beforelaunch in somecountries and after launchin others.



• 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


Risk-Sharing & Performance-BasedAgreements


Performance-Based & Risk Sharing Agreements

• Pay for performance• Old concept…



• 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



• 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



• 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



• 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



• 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)



• 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?



47

22

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


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).



• 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



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



Health Plan Claims

InpatientChart

Information

OutpatientChart

Information


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


Changing the Drug DevelopmentParadigm


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)


European HTA Collaboration


European HTA collaboration

ICT: Information and Communication Technology


Health Economics in the Context of PersonalizedMedicine


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



• 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.



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



• 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


Valuing TargetedTherapies


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!)



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?



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?



GEP: Gene Expression Profiling


Market Access Modelization



• Context: MCDM/MCDA: Multi-Criteria Decision Making/analysis@ Roche for Market access & HTA modelization


Value-Based PricingAcross Indications


VBP across Indications

• Company Perspective



• HTA Perspective (Italy)


Conclusion


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

ispor 19th international meeting - · pdf filepharmacoeconomics. should: • drive clinical...

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