Welcome to the second in a short series about a global pharmacoeconomics (defined in the first post) conference held in May, 2018. The first post overviewed ISPOR (the sponsoring organization), the general intent and nature of the conference, and what pharmacoeconomics does. For more information on ISPOR: www.ispor.org. Today we focus on a key conference theme: value-based (payment) arrangements (VBA), also known as performance-based risk-sharing arrangements. This is a big topic because the recent market entry of very high cost potentially curative treatments begs for some mechanism to link cost and value = cost per desired outcome, such as a quality-adjusted year of life, a “QALY”). (1)

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What we'll touch on today:

• The rationale for linking payment to value
• Why the trend to do so now?
• VBAs collect real-world data (RWD). Ancillary effect: Resolving uncertainties with real-world evidence (RWE)
• Should YOU engage in a VBA? (more in future deep dive)
• If so, what factors drive success and what are the ‘gotchas?’
• The VBA sandbox (more in future deep dive)
• VBA checklist (more in future deep dive)
• What VBA resources can I access?

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Last episode, we framed pharmacoeconomics as the science (and art) of defining and measuring the value of clinical interventions, mainly those involving drugs and devices. We noted that the kind and quantity of value differs depending on the perspective. For example, society might pace a high value on an early health intervention that improves education attainment level (because it leads to greater lifetime earnings and prepares the populace for a technologically-advanced future), but an insurance carrier is likely to view that as low-value for their money and out of their scope.

The range of perspectives includes patients, caregivers, clinicians, the general public, politicians or governing bodies, healthcare institutions, drug and device manufacturers and payers (private and public; employers)--but today (like most of the ISPOR VBA sessions) we’ll focus on VBAs between U. S.  healthplans (payers) and drug manufacturers. This episode focuses on the ISPOR conference; we’ll dive deeper into the topic of value-based agreements in our regular blog posts.

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Conference presentations directly related to VBA (including generation of real-world evidence--RWE--as an important ‘gift’ of VBAs):

• A half-day pre-conference course
• Panel on advancing access to innovative health tech in Asia-The role of real-world data (RWD) in a value framework
• Panel on value-based arrangements and value demonstration in Latin America
• Using RWE in regulatory and coverage decision-making: The National Evaluation System for Health Technology Coordinating Center, NESTcc (their focus is on devices), based on a voluntary data-supplying collaboration
• Panel on what research should be undertaken to support decisions regarding the value of curing or eradicating disease (Gets at the ‘cost-effective but unaffordable’ question)
• Principles of effective machine learning applications in RWE

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VBA 101: What’s a value-based agreement? Value is cost per valued outcome; both numerator and denominator of this equation differ depending on perspective (for today, that perspective is US healthplans; cost is money--prevented spending--and the ‘valued outcome’ is a preventable health event that can be linked to money (monetizable), such as hospitalization for a heart attack or a marker that’s known to be linked to such an outcome such as LDL cholesterol level in a patient at high risk for a heart attack). A VBA links payment for a drug (or device) to its performance relative to the outcome of interest.

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The ISPOR Performance-Based Risk-Sharing Arrangement Task Force’s 5 key characteristics defining a VBA:

1. The VBA program is intended to address uncertainty about efficacy or effectiveness in a population compared to current standard of care
2. A mutually agreed program of data collection
3. Data collection initiated following regulatory approval and linked to post-launch coverage decisions
4. Price/ reimbursement/ revenue linked to the outcome of the data collected
5. Provides a different distribution of risk between payer and manufacturer than in the traditional relationship

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A pre-conference course: Performance-based risk-sharing arrangements for drugs and other medical products (2)

Our instructors were Louis P. Garrison, Jr., PhD (Professor, Pharmacoeconomic Outcomes Research & Policy Program, Department of Pharmacy, U. of Washington, Seattle, WA); Adrian Towse, MA, MPhil (Director, Office of Health Economics, London, UK); and Jack J. Carlson, MPH, PhD (Associate Professor, Pharmacoeconomic Outcomes Research & Policy Program, U. of Washington, Seattle, WA). All are heavy-hitters in the field and excellent instructors.

The course covered current practices and trends; economic incentives that can serve as the basis for a VBA; and evaluating opportunities to develop and implement VBAs. We examined the nature and distribution of VBAs around the globe, noting that most of what we know about VBAs comes from outside the US, because the structures, details, and outcomes (and often even the existence) of US VBAs is usually proprietary. Presentations at the main conference reinforced this; nevertheless, we can glean much that’s potentially relevant to the US situation from the global landscape plus what we do see publicly here.

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Two factors driving the rising interest in VBAs: High cost and uncertainty about outcomes. Highly-sophisticated treatments based on modifying or targeting the immune system (immunotherapy), cell signaling pathways (in health needed for damage repair, growth and regeneration; but run amok in cancer), or faulty genes seem to promise a brave new world of highly effective personalized treatment--but are expensive to develop and bear considerable market risk from competitors, poor real-world effectiveness, and adverse effects not seen in clinical trials. Manufacturers’ pricing decisions need to take development cost and market risk into account (not to mention the need to fund future research). Thus the extreme price of many new therapies--tens to hundreds of thousands of dollars.

Who’s going to pay these prices? For the most part, healthplans, whose budgets are threatened, and who are assuming the risk that the shiny new drug may not work as well as in clinical trials (or that even if it does, it’s still not worth the price, especially if the benefits mainly accrue over the years when the patient’s covered by a different healthplan).

Uncertainty about outcomes in the real world is a big driving force here. The drug was tested in the highly controlled world of the clinical trial, on a relatively small number of patients, usually for several months to a few years. Meanwhile, health outcomes are the result of complex interacting players and processes that cause uncertainties in the relationship between an action (such as using a drug) and outcome. Early on the uncertainties are large--including how well the treatment works compared to alternatives (very often the clinical trial compares drug to placebo, though not so much in oncology)

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Postmarketing analytics and pragmatic studies can reduce uncertainty by collecting data from the messy real world (RWE). But manufacturers have limited incentive to do so. A VBA carefully specifying the collection of RWD (related to and--importantly--beyond the contract’s performance outcomes) can help reduce that uncertainty, potentially leading to more rational pricing (which could be higher or lower depending on performance).

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More like this to come, increasingly unveiling the opportunity, effectiveness and the cost of extreme precision medicine. So far, these treatments affect relatively few patients (in the “long tail” below), but what if they began to affect the “short head” of health conditions affecting many thousands or millions of people? (Think: cure for diabetes, hypertension or rheumatoid arthritis)

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Our instructors offered four options for payers faced with a new high-cost therapy with as-yet considerable effectiveness and longer-term safety uncertainty:

• Adopt with no further evidence (“yes”)
• Refuse to adopt and seek more evidence (“no” or “use in research”)
• Mandate a lower price (reduce uncertainty about incremental value compared to existing treatments)
• Enter into a VBA (“yes but” - conditional reimbursement) that’s either an outcomes-based coverage with evidence development or an individual patient reimbursement in which only some patients will be paid for (those who achieve a performance goal)

Coverage with evidence development(CED) brings risks to both payer and manufacturer (and patient, who may ultimately have been better off with existing treatment). And, coverage decisions can be hard to reverse. Still, CED combined with risk-sharing can lead to rational price adjustments. And even a “yes, we’ll cover that” can be based on a reasonable estimate of incremental benefit over existing therapies; supplying such estimates based on best current evidence with transparent, multi-stakeholder input is the mission of ICER--the Institute for Clinical and Economic Review (https://icer-review.org/). We’ll dive into this organization’s work in a future episode on value-based arrangements.

We reviewed several examples of performance-based payment schemes--mainly outside the US where public-payer entities are charged with adhering to a fixed budget and are permitted to negotiate price at the federal level. An example: A UK “stopping rule” conditional payment scheme for Velcade(R) (ixazomib), an immunotherapy for multiple myeloma--no payment if the biomarker, serum M-protein (3), failed to decrease at by at least half after 4 treatment cycles; responding patients received 4 additional cycles at full (negotiated) price. Another example of a stopping rule regarding PCSK9 inhibitors for very high cholesterol in high-risk patients: no payment if the patient has a heart attack (the drug’s potentially preventable event) within a prespecified time. In another example in Italy, a new Alzheimer’s drug was provided free for a few months. If the performance outcome was met, the patient could be continued on the drug at the negotiated price. Not all schemes involve all-or-nothing payments: Price could vary up or down retrospectively based on whether (or how well) clinical targets such as A1C or LDL cholesterol were met.

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As we’ll dig into in a future episode, there are a number of potential concerns or ‘gotchas’ when developing a VBA:

• One incentive for the manufacturer is to increase access to (and use of) their product. This might occur through adherence programs or the relaxing precertification controls. The risk to the payer is that some patients put on drug might not be fully clinically appropriate. In this situation, payers may continue to require precertification.
• Performance metrics must be or be clearly related to valued (and in the US, monetizable) outcomes, be clearly specified (4), and be based on data that’s available and straightforward to capture (5)
• Using the types of data that will be used to track and adjudicate the VBA, a retrospective analysis should be done to get a sense of the number of healthplan members potentially involved (meet the criteria to be included in measurement), their adherence and clinical status generally and in relation to the contract’s likely outcomes metrics
• Must be adequate resources to develop, implement, analyze and report on the VBA
• Both parties’ willingness to collaborate, discover and develop a transparent auditable process and operate in a learning environment (we probably don’t have to tell you that this is easier said than done) (6)

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A bottom line: “Overall the literature suggests there is an important gap in structured ex-post evaluations of (VBAs). Utilization schemes appear to have been more successful to date than coverage with evidence determination schemes. However, the evidence is limited, mixed, qualitative and partial.”

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A VBA modeling system: The VBA “sandbox”

We foreshadow a future episode wherein we present the idea of modeling the consequences of a VBA from the manufacturer and payer standpoint, using a range of plausible assumptions about outcome metrics, their baseline and post-treatment states, number of qualifying patients, and treatment costs. The sandbox illustrates the importance of explicit agreements on the performance metrics and their definitions (numerator, denominator and how to qualify for each, using what data), as well as a way for both parties to understand what factors drive potential wins and losses. We’ll dive into a published model--if you want to read ahead, it’s Brown JD, et al. Payer and pharmaceutical manufacturer considerations for outcomes-based agreement in the United States. Val Health 2018;21:33-40.

And, as always, we’d love to hear from you--in the Comments below, or by contacting us HERE.