Real-world data is Liisa’s world. After receiving her PhD in health policy and administration from Penn State University some 20 years ago, she joined Thomson Healthcare (now part of Merative) and has worked in outcomes research ever since.
Individuals who spend time around clinical trial sponsors are likely to hear the words “real-world data” discussed as a kind of cure-all for creating more efficient trials. While real-world data has shown tremendous potential since it was first cleared for use by the FDA in 2016 through the Cures Act, its role in trials today often remains more aspirational than actual. If real-world data is really so effective, why aren’t more contract research organizations (CROs) using it? The problem stems in part from the fact that real-world data can mean a lot of different things, not all of which are well-suited to clinical trials depending on their scope and objective.
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In the healthcare ecosystem, there is a wide variety of data: electronic health records, prescription data from pharmacies, insurance data, administrative claims data from self-insured employers, health metrics from wearables and so on. Each of these data sources has their own special vantage point of the patient. An oncology center, for example, may have detailed information on tumor sizes, progression-free survival rates and other clinical data. A pharmacy will collect data on a patient’s prescriptions and treatment costs within their field of service. Most of these data sources have limits to their longitudinal view of the patient. An insurance carrier would only have visibility into patient claims so long as the patient is an active customer. If a patient switches insurance providers (or, in our earlier example, switches pharmacies), that visibility ends.
Administrative claims data collected from self-insured employers is often viewed as an excellent source of longitudinal patient data. Because these employers are responsible for all employee medical payments, their claims data spans across insurance companies, hospitals and pharmacies. According to the Kaiser Family Foundation, roughly two-thirds of all employees are covered by a self-insured employer1 — a sizable pool of patient data from which to draw.
Increasingly, real-world data — and administrative claims data specifically — is being evaluated to support activities in clinical development. The data can identify a priori criteria that may make patient recruitment more challenging, with the potential of avoiding protocol amendments. The data can be used to ensure that trial participants reflect the diversity/equity profiles of those within the field of study. Even the creation of external control arms is possible using administrative claims data.
Let’s take a closer look at a few examples where real-world data can have a real-world impact on clinical trials. Optimizing trial effectiveness is one of the best use cases for real-world data in trial development. Studies have shown that the majority of all clinical trial protocols need to be amended, frequently because, as written, the protocols limit the potential pool of participants by a significant amount. For example, imagine you’re a CRO conducting a trial of a new medication. If the protocol specifies that participants cannot have hypertension as a comorbid condition and 80% of patients in the field of study have hypertension, you know you need to either expand your field of search or plan for an extended period of enrollment to account for the large number of ineligible participants. Real-world data could help you identify geographic regions where the incidence of hypertension is lower to reach the requisite number of participants.
Prescription label expansion is another compelling use case for real-world data. As pharmaceutical companies look to expand FDA approval for additional areas of treatment, RWD can help identify new groups that might benefit from the drug treatment and even identify patients outside the traditional audience who are already receiving (and benefiting from) the drug treatment. The classic example here is the use of RWD and evidence to support the use of palbociclib in males with breast cancer.
A third use case is the creation of external control arms for clinical trials. Oncology departments in particular have found real-world data to be effective in creating accurate control arms. The data used in external control arms must closely match other sample populations and often requires highly detailed information, which a CRO or trial sponsor may not have within their own data sources.
It's important to remember that, while integrating real-world data into the clinical development process has the potential to be a game-changer for CROs, it is not a panacea that will eliminate all challenges from the trial development process. Companies still need to do their due diligence in selecting the right data choice and data vendor, a decision that will ultimately be driven by the trial design itself.
So, what should you be looking for in a real-world data solution? Read my next blog to find out.
See what’s possible with Merative’s real-world evidence solutions.
