There has been a shift in the global healthcare ecosystem from volume-based to value-based payment model, thanks to a surge in data availability, interoperability, advancing health technologies, cost and competitive pressures, scientific advances, and increasing adoption of personalized medicine. The resulting availability of a large quantity of real-world data (RWD) has made it possible to perform continual observation of disease epidemiology, treatment patterns, and outcomes in the real world. Analysing strong RWD generates strong real-world evidence (RWE), and the incredible power of RWE in the drug approval process, including prioritizing and streamlining drug development, is being realised by all stakeholders. RWE especially gains importance because randomized controlled trials (RCTs) cannot be applied to the entire patient population of a specific disease. Parallel to this, the value, usage, and acceptance of RWE in the pharmaceutical and biotechnology industries have also increased in recent years.[1]

RWE is increasingly used by the regulators in the drug and device approval cycle, for safety evaluation, updating label claims, and for new usage approvals, as a supplement to RCTs for improved understanding of efficacy and safety of medical products and devices. However, the main concern in the usage of RWE lies in the robustness and quality of RWD. Since RWD is mined under uncontrolled settings from data often collected without a pre-defined objective, there is a possibility of data inconsistencies and spurious results, leading to a relatively lower quality of data compared to RCT data. Data quality has been defined to ensure conformance, completeness, and plausibility,[2] and to achieve high quality of RWD, there is a need for uniform regulatory guidelines and frameworks surrounding RWD collection and analysis.

Globally, regulatory bodies are showing interest in adopting RWE as a component of the decision-making process to complement RCT evidence by strengthening the guidelines and framework for including RWD. For example, in the USA, the 21st Century Cures Act and Prescription Drug User Fee Act recommend the use of RWE, as a supplement to RCTs evidence, for regulatory decision-making and approval of drugs. In December 2018, the USFDA released a framework for the USFDA’s RWE program for evaluating the potential use of RWE for approval support to drugs and biologics.[3] The key considerations in the USFDA RWE program are: RWE must be ‘fit for use’; trial/study designs should provide adequate scientific evidence; and RWE must comply with the USFDA regulatory requirements. [4] In addition to the USFDA’s efforts, several other initiatives, such as the Clinical Trial Transformation initiative, Friends of Cancer research, are working to optimize RWD, developing new study methods, and refining RWD analytics.[3,5]

In the UK, an RWD framework has been structured to ensure that the collected RWD is of relevance, provenance, and sufficient quality. The framework ensures relevancy, transparency at all levels of study planning, conduct, and reporting, and robust analytics to minimize bias and uncertainty. This living framework is being periodically updated based on user feedback and practice.[6] In Europe, the EMA launched the OPTIMAL (OPerational, TechnIcal, and MethodologicAL) framework in 2019 to explore the pertinent use of valid RWE for regulatory purposes.[7]

Similarly, Health Canada is working with the Canadian Agency for Drugs and Technologies in Health (CADTH) and the National Institute of Excellence in Health and Social Services (INESS) to establish a joint document to optimize the use of RWE. In Japan, the Pharmaceuticals and Medical Devices Agency (PMDA) established regulatory guidelines in March 2021 for the use of registries, to ensure the reliability of RWD and RWE.[4]

In addition to these efforts, the ICH-GCP has also established plans to harmonize global RWE and update its existing E6 (General considerations in clinical trials) and E8 (Guidelines for Good Clinical Practice) guidelines. The CIOMS (Council of International Organisations of Medical Sciences) is currently developing a consensus report and recommendations for the use of RWE in the regulatory decision-making process.[4]

The shift from restricted uses of traditional evidence sources (RCT) to wider adaptation of newer modes (RCT + RWE) in different regions of the world is a positive sign showing a global increase in the value of RWE in research, practice, and policymaking. RWE undoubtedly serves as a key to a more robust, less expensive, and more inclusive approach to better healthcare through research. Undoubtedly there are gaps in the use of RWE at present: some countries have more acceptance and activity than others. There is a need for development of realistic and robust standards and best practices to ensure the quality of RWD used in RWE. Recommendations and uniform guidelines are needed across the world to shape, harmonize and generate reliable RWE. Nonetheless, the future holds a good promise on RWE.

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References:

  1. Khosla S et al. Real world evidence (RWE) – a disruptive innovation or the quiet evolution of medical evidence generation? F1000Research. 2018;7:111.
  2. Kahn M et al. A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2016;4(1):18.
  3. Framework for FDA’s real-world evidence program. 2018. Available from: https://www.fda.gov/media/120060/download
  4. Burns L et al. Real-world evidence for regulatory decision-making: guidance from around the world. Clinical Therapeutics. 2022;44(3):420-437.
  5. Oncology Real World Evidence Program. USFDA 2021. Available from: https://www.fda.gov/about-fda/oncology-center-excellence/oncology-real-world-evidence-program
  6. The NICE strategy 2021 to 2026. NICE 2022. Available from: https://www.nice.org.uk/about/who-we-are/corporate-publications/the-nice-strategy-2021-to-2026
  7. Big Data Steering Group workshop. Available from: https://www.ema.europa.eu/en/documents/work-programme/workplan-hma/ema-joint-big-data-steering-group_en.pdf  

 

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