Proceedings: DIA 2018 Global Annual Meeting

Triple-A RWE:
Adequate Data, Appropriate Study Designs, and Actionable Evidence

Sandra Blumenrath
DIA Science Writer


he last several years have seen increasing efforts to better understand when and how real world data (RWD) and evidence (RWE) could better support a wide range of regulatory decisions. And recent legislation in the US, such as the Prescription Drug User Fee Act VI and the 21st Century Cures Act, includes provisions that require the FDA to explore further uses of RWE and its potential value within the regulatory framework. At the DIAmond session “Triple-A RWE: Adequate Data, Appropriate Study Designs, and Actionable Evidence,” representatives from research institutions, industry, and FDA discussed guiding principles regarding the selection of adequate RWD sources and appropriate study designs within the regulatory context.

Key Takeaways

  • RWE provides an opportunity for fast data collection on a broad patient population to identify new indications and rare adverse effects, define standard of care, support label expansion, and to better understand efficacy. The use of RWE is also needed in some orphan diseases where randomized controlled trials (RCTs) are neither practical nor ethical.
  • RWD includes information on various endpoints that are meaningful to patients but may not be collected in clinical trials (CTs), such as health status or longer-term endpoints.
  • The biggest challenge is to overcome long-held biases and to reach scientific consensus on reliable, high quality RWD appropriate for regulatory approval, and ways to curate data from various sources.
  • Current initiatives, such as the FDA’s Sentinel System, the Duke Margolis Center’s new RWE Framework as well as various pilot projects, can reduce uncertainties and provide frameworks for the use of RWE.
  • For now, RWD and RWE are best used in support of CTs to complement clinical findings.

Thomas Bols and Gregory Daniel recapture some of the challenges and opportunities associated with RWE that were discussed during the session.

Bridging the Gap Between Clinical Trials and Clinical Practice

A drug that has been on the market for a long time has a wealth of information on long-term efficacy and effectiveness, adverse events and safety, off-label use, and early treatment termination. Panelists agreed that when RWE is used in combination with RCTs, it builds regulatory evidence that is much richer than the clinical evidence alone, providing a more complete picture of a treatment’s benefits and long-term risks.

RWE also enables us to identify new indications and generate deeper knowledge about a wider range of patients, including special populations, who more closely resemble the patients who physicians see in their offices. The panel pointed out that this may be especially the case when there are differences between efficacy and effectiveness. Typically, these differences are driven by certain patient characteristics that differ between CTs and the real world, such as age, disease severity, and comorbidities. They can also be a result of how the intervention is implemented in practice.

Although randomization is still important, when assessing how an intervention works in real world practice, RWE becomes a crucial complement to CTs.

Left to right: Gregory Daniel (Moderator), Cathy Critchlow, Paul Bleicher, Pall Jonsson, Jacqueline Corrigan-Curay

DIAmond Session Panelists
Paul Bleicher, Chief Executive Officer, OptumLabs

Jacqueline Corrigan-Curay, Director, Office of Medical Policy, CDER, FDA

Cathy Critchlow, Vice President, Center for Observational Research, Amgen Inc.

Gregory Daniel (Moderator), Deputy Director, Policy, Duke-Margolis Center for Health Policy

Pall Jonsson, Associate Director, Research and Development, National Institute for Health and Care Excellence (NICE)

Many of the challenges with RWE are related to questions on data quality and observational study design that still need to be fully addressed to achieve fit-for-purpose RWE in regulatory decisions:

  1. RWD can come from various types of sources and are often curated using different, non-standardized methods. How do we know that the data that were used to generate RWE were of high quality, and what metrics should we use?
  2. Observational studies harbor uncertainty. How do we know that these studies are credible and reliable?

Unfortunately, the general mistrust in observational data and the lack of scientific consensus on what constitutes high-quality data often complicate the debate. However, the panelists saw a role for industry to produce the kind of rigorous information and data that will mitigate some of these reservations.

Databases such as electronic health records and claims databases seem to hold the greatest promise in delivering the type of data that would create a robust RWE dataset. However, a lot of information is locked up in narratives and difficult to extract despite advances in natural language processing. And in unusual diseases cases, claims data may not be granular enough for regulatory approval.

From a regulatory perspective, combining, organizing, and analyzing data from various sources remains a major challenge that needs to be addressed with a standardized data curation approach. Differences in healthcare outcomes are often related to behavioral, social, and environmental factors. For RWE to support regulatory decisions, it is important to capture and weigh these confounding variables in observational studies.

Where Are We Now?

Significant progress has been made with collecting and using RWD to support regulatory decision making, although the application of RWE in healthcare is still quite limited. The pharmaceutical industry, FDA, and Congress have recognized the need to promote the use of RWE, and various guiding documents and frameworks have already been published.

As a result of the 21st Century Cures Act, the FDA is tasked with issuing guidances on the use of RWE for two specific cases: (1) to support a new indication or labeling changes for a drug that is already on the market, and (2) to support required post-market studies of drugs that were approved under accelerated approval pathways. With the FDA Sentinel Initiative, regulators have also attempted to reduce uncertainties with regard to the curation of real world datasets.

The Duke Margolis Center for Health Policy developed a RWE Framework to guide sponsors and FDA in RWE discussions, putting forward near term steps on priority issues. It lays out the current RWD/RWE landscape and the potential process that stakeholders should go through when assessing RWE approaches for regulatory use.

The panel also highlighted the importance of pilot studies in the development of data curation processes and validation frameworks that standardize RWD collection. With pilot projects it is possible to evaluate the performance of real world endpoints across multiple datasets. The panelists emphasized that the best pilot project complementing CT evidence with RWE starts with well-studied diseases and treatments for which there are a lot of reliable CT data. Methods, endpoints, and important covariates can then be easily validated.