Real-world evidence (RWE) is derived from real-world data (RWD) sources, such as electronic health records (EHRs), claims data, data from product/disease registries, pharmacy data, social media, and pragmatic trials. RWE provides essential insights into the clinical experience, thereby complementing the information obtained from traditional randomized controlled trials (RCTs).(1)
RWE has been extensively used for post-marketing safety observations. Realizing the increasing importance and utility of RWE in the drug approval cycle, the USA passed the 21st Century Cures Act in 2016 which allowed pharma companies to utilize RWE to support drug approvals and update label claims. Subsequently, in 2018, the USFDA rolled out a draft guidance for submission of RWD for assessment of investigational new drugs (INDs), and new drug applications (NDAs).(2, 3) This recommendation represents a way to optimize the use of RWD and RWE and it will help standardize the applications with RWE for market approvals.(1)
Traditional RCTs are typically aimed at measuring the safety and efficacy of interventions, compared with standard treatment (or placebos), usually in double-blinded settings, and recruit a closely targeted population. This is done to minimize bias and confounding factors.(4) Traditional RCT findings are still the gold standard for regulatory approval of a medicine, for the expansion of a product label, and to support treatment guidelines.(4) However, these trials are expensive, and the patients enrolled in controlled settings often do not represent those in everyday clinical practice.(5) Thus, clinical research professionals are always looking at methods to optimize clinical trials.
With the increasing interest in RWD, a new method has emerged to incorporate RWE to optimize RCTs. For certain RCTs, previously collected RWD is used to prepare ‘synthetic’ control arms, which replace conventional ‘control’ group in RCTs. For instance, the USFDA approved Merck’s Bavencio (avelumab) in 2017 for metastatic Merkel cell carcinoma, which was based on a single-arm trial and a synthetic comparator arm that used historical control of matched patients. Roche expanded access to the treatment for non-small-cell lung cancer [Alecensa (alectinib)], making it available in 20 European countries, by using synthetic control data.(6)
The benefits of synthetic control arms include lower study costs, reduced delays and faster access to drugs. The use of RWE for creating synthetic control arms is still in its early phase. New technologies are being explored to assist in extracting relevant information, ensuring data quality, and ultimately for bringing out the full potential of this approach to the forefront, by never completely replacing RCTs, but by leveraging RWE. Specific care practices are encouraged to achieve advanced accuracy. Hence, all the stakeholders must work towards defining standards for ensuring quality.(6)
To facilitate integrating RWE with the RCTs, some stakeholders have laid down recommendations for preparing RWE suitable for regulatory decisions. For instance, the white paper launched in 2017 by Duke-Margolis Center for Health Policy, with support from the USFDA, talks about the regulatory use of RWE. This white paper was released after an open consultation with stakeholders, including academics, patients, and the industry.(7) Moreover, several areas have been identified for practical improvement of RWE that would fit regulatory decision-making. These include matching RWD sources with appropriate study designs and data collection, enhancing methods to address the research question, and transparent collaborations while sharing datasets. Professional societies, such as the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the International Society for Pharmacoepidemiology (ISPE) have also jointly published recommendations for good procedural practices for RWE aimed at building confidence about and expanding the current use of RWE in health care decision-making. (8, 9)
Study designs using RWD is a much-needed yet logical step in advancing the system of regulatory approvals. However, it will require collaborations among regulators, pharmaceutical companies, and RWE experts to optimize the scientific potential of RWD through innovative study designs which would generate solid and dependable RWE.(9)
Become A Certified HEOR Professional – Enrol yourself here!
- Naidoo, P, et al. Real-world evidence and product development: Opportunities, challenges and risk mitigation. Wien Klin Wochenschr 2011; 133:840-846.
- US Food and Drug Administration. Framework for FDA’s real world evidence program. 2018. Available at: https://www.fda.gov/media/120060
- S. Department of Health and Human Services, Food and Drug Administration. Submitting Documents Using Real-World Data and Real-World Evidence to FDA for Drugs and Biologics Guidance for Industry. 2019. Available at: https://www.fda.gov/regulatory-information/search-fdaguidance-documents/submitting-documents-using-real-world-data-and-real-world-evidence-fda-drugs-and-biologics-guidance.
- Katkade VB, et al. Real world data: an opportunity to supplement existing evidence for the use of long-established medicines in health care decision making. J Multidiscip Healthc 2018; 11:295-304.
- Xia AD, et al. RWE Framework: An Interactive Visual Tool to Support a Real-World Evidence Study Design. Drugs – real world outcomes 2019; 6(4):193-203.
- CHR Sparks. Synthetic control arms – use of RWE in clinical trials. Available at: https://www.camhcr.com/blog/synthetic-control-arms-use-of-rwe-in-clinical-trials
- Duke-Margolis Center for Health Policy. A framework for regulatory use of real-world evidence. 2017. Available at: https://healthpolicy.duke.edu/sites/default/files/atoms/files/rwe_white_paper_2017.09.06.pdf.
- Berger ML, et al. Good practices for real-world data studies of treatment and/or comparative effectiveness: recommendations from the joint ISPOR-ISPE Special Task Force on Real-World Evidence in Health Care Decision Making. Value in Health 2017; 20(8):1003-1008.
- Andre EB, et al. Trial designs using real-world data: The changing landscape of the regulatory approval process. Pharmacoepidemiol Drug Saf 2020; 29:1201-1212.