Real-World Decisions
In essence, payers make formulary management/medical policy decisions based on three criteria: efficacy, safety and cost. When a new product launches, payers are largely dependent upon manufacturer-sponsored clinical trials for safety and efficacy data. Yet clinical trial results do not satiate payers’ hunger for evidence. Real-world evidence is positioned above clinical trial results on payers’ hierarchy of needs. Yet despite this, payers knowingly make initial market access decisions with limited, incomplete data. Of course, new decisions are made as new evidence becomes available.
Trials and Tribulations
Clinical trials are carefully designed to demonstrate the efficacy and safety of a product compared to a placebo or, better yet, an active comparator. Insights based on a well-designed randomized controlled trial (RCT) are considered internally valid – the observed differences are a result of the experimental design. However, even well-designed clinical trials with endpoints of clinical or economic importance, comparators of interest, and large samples sufficient to detect smaller effects and support relevant subgroup analyses yield results that may not be generalizable or externally valid.
Some inherent features of RCTs threaten the generalizability of the data. Payers often cite how inclusion and exclusion criteria may make the studied population different than the general population or, more importantly, a payer’s population of covered lives. And the potential bias introduced by pretests, a hallmark of RCT designs, can similarly threaten the external validity of results. Just as a little air is lost while checking the pressure in a tire, measurement can impact that which we measure. Additionally, reminder calls and follow-up – both artificial aspects of clinical trials that impact compliance and help prevent dropouts – can yield results that differ from what is ultimately observed in the real world.
Many products initially deemed safe and effective by the FDA based on clinical trial results have later proven to be ineffective or hazardous, and recalled or given a black box warning. And although they know extrapolations from clinical trials can be terribly wrong, payers try to suspend their skepticism during post-launch as they watch and wait.
Where Real-World Evidence Comes From
Payers often wonder how a drug will perform for their population, which may be substantially different from the studied population on known demographic, socioeconomic and geographic dimensions, and unknown attitudinal, behavioral and genomic dimensions. Payers need real-world evidence to answer this question.
By definition, real-world evidence requires experience outside purposefully artificial clinical trial confines and controls. Broader use and extended periods are necessary, but not sufficient. Real-world evidence must also be collected, analyzed and reported, and doing so requires access to the data, technical expertise, resources, including funding, and, perhaps most importantly, motivation.
Manufacturers may not make voluntary investments in real-world evidence because the perceived costs outweigh the benefits. After all, there is a risk that the superior performance demonstrated in trials is lackluster in the real world. Alternatively, a manufacturer may find new contraindications, drug-drug interactions or adverse events – information that can help the public but hurt sales.
Regulators may require postmarketing surveillance through a REMS program, but this is typically done when there are significant safety concerns. Academics are eager to collect, analyze and report real-world evidence, but often lack the funding to do so. And while patient advocacy organizations are increasingly providing support for such research, this often does not offset the diminishing funds available from governmental agencies.
Alternatively, payers may derive real-world evidence from their own ‘big data’ for prevalent conditions. Doing so eliminates questions of generalizability. However, even the largest payers may not cover enough lives to find the signal in the noise for thousands of rare conditions. Payers covering millions of lives may only have a handful of members with a particular rare condition.
Competition and Cooperation
Competition among manufacturers discourages data sharing for products in the same drug class. Yet cooperation among firms may make sense when the shared objective is to demonstrate superiority of one class over another – although manufacturers with a portfolio in a given therapeutic area would be reluctant to support such research if it cannibalizes their own products.
Payers similarly compete with one another – for clients, members and, to a lesser degree, providers. Despite these rivalries, the advantages of cooperating on real-world evidence initiatives may exceed the disadvantages under certain conditions, like the small numbers problem associated with rare conditions that preclude a self-reliant approach. However, just as smaller payers are disadvantaged by their small numbers problem, they also bring less to the table as a potential partner. Any voluntary cooperation among payers would then likely be limited to the minority of payers that cover the majority of lives, or, perhaps, a large coalition of smaller payers.
As the World Turns
Real-world evidence is a journey, not a destination – the competitive set, clinical practice, patient population characteristics and evidence itself are forever in flux. The paucity of real-world evidence despite demand by payers and other stakeholders reflects a market failure. Over time, this void may be filled by new postmarketing surveillance requirements or incentives by purchasers and regulators, initiatives led by employer coalitions, cooperation among and consolidation of payers, and patient registries established by patient advocacy organizations. Only time (and data) will tell.
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