Key elements of a real-world evidence generation plan: integration of product support activities

Real-world evidence is a key component of value demonstration for pharmaceuticals, diagnostic agents, and medical devices throughout the product lifecycle. Planning for real-world evidence needs early in the pre-launch period can allow for seamless integration of health economics and outcomes research to support product value and market access.

1. Start with the “aspirational” product value story and work backwards

It is necessary to plan your real-world evidence generation strategy early in the product development process, so that the right and most impactful data can be collected and analyzed to support the product’s value story. A set of aspirational value messages can help guide evidence generation strategies, including those for real-world evidence. A literature-based “match and gap” analysis can be used to determine whether aspirational messages are supported by existing literature, and then evidence generation strategies can be tailored to fill key data gaps.

Choosing the appropriate study design and planning for the time required to execute are important. Secondary data sources (such as administrative claims data) are readily accessible, and analyses can be completed in 3 to 6 months, but essential elements such as disease staging and severity are often missing. Primary data collection is often required to obtain data elements for stratification of patients, such as biomarker results or rational for treatment decisions. Retrospective chart reviews can generate results in 6 to 12 months and can be combined with a patient-reported outcome (PRO) survey in a hybrid design to provide a snapshot of quality of life or patient preference. Prospective study designs usually take 12 months or longer to generate results but create opportunities to collect longitudinal quality of life data.

2. Establish the disease burden

Demonstrating the clinical, economic, and humanistic burden of a disease is a key need early in the development process and sets the stage to inform stakeholders regarding the potential value of a product. For rare diseases, this can mean raising awareness of the disease, estimating incidence and prevalence, and characterizing the population with the disease. Data for more common illnesses may be available but out of date. Estimating the economic burden of disease is especially important to a payer audience, in order to justify resources required to adopt new technologies.

Real-world evidence on the epidemiology and burden of disease should be communicated in peer-reviewed publications, incorporated into global and payer dossiers, and used to populate economic models. An accurate estimate of disease incidence and prevalence is crucial for to understand the budget impact of the adoption of new technologies.

3. Identify current treatment patterns and unmet needs

Understanding the current standard of care for the target patient population and, by extension, the unmet patient needs expected at the time of launch, is essential for an informed market access strategy. Retrospective studies to identify current market share are required to populate budget impact models and can be used to drive the selection of comparators for cost-effectiveness models.

Further exploration of patient characteristics and clinical outcomes associated with current treatments can support a value narrative centering on unmet needs. For example, real-world evidence of early treatment discontinuation due to adverse events with existing therapies can support value messages for products with improved adverse event profiles. Such a finding could also highlight the need for the integration of qualitative research or patient-reported outcomes assessments in your real-world evidence generation strategy to further characterize the burden of adverse events on patients.

4. Determine the key economic drivers

A solid economic value story is crucial to reimbursement in some countries and plays a supporting role in others. As such, data needs for economic models are a key consideration for real-world evidence generation plans.

The scope of real-world evidence needed depends on the complexity of the disease process and its treatment, and in turn the complexity of the model. Real-world studies can be used to quantify the cost of treatment success or failure, stratify medical costs by disease severity, and identify the incremental cost of adverse events.

Early economic models, typically designed and executed during phase II development, can be powerful tools for ensuring a solid evidence base for subsequent payer-facing models. Gaps in data required to populate cost-effectiveness and budget impact models, such as treatment pathways and healthcare resource utilization, can be identified early and addressed using real-world studies.

5. Support clinical effectiveness

From a product development perspective, the role of real-world studies has historically been limited to the post-marketing setting, in which they have been used to fulfill post-marketing commitments, confirm safety and effectiveness in a broader patient population than that studied in clinical trials, and assess comparative effectiveness of treatments. These studies are important components of an evolving value proposition for a product, which can be communicated to decision-makers in the form of publications and incorporation into value dossiers.

In the United States, the 21^stCentury Cures Act has the potential to significantly reshape how real-world evidence is used in the development of new health technologies. In December 2018, as required by the Cures Act, the United States Food and Drug Administration (FDA) issued the Framework for FDA's Real-World Evidence Program, with guidance for the use of RWE in regulatory filings to be issued by the end of 2021. Similar guidance was issued in 2017 for medical devices, noting that real-world data could be used for purposes such as generating hypotheses to be tested in prospective studies, as a historical or concurrent control group, and as evidence to support biomarker validity, among others. 

Conclusions

Real-world evidence can support product value directly, by raising awareness of the disease burden and unmet needs, and indirectly, by providing inputs for economic models and identifying opportunities for additional qualitative or patient-centric research. Understanding the role of real-world evidence in the overall product value strategy and coming up with a solid plan early in the product development process can help justify the need for resources to conduct these studies in the face of competing priorities.

Contact OPEN Health for more information on Real-World Evidence.