How to leverage registry-based studies to generate high-quality Real-World Evidence

The use of Real-World Evidence (RWE) in regulatory decision making will support the development and application of better medicines. That’s why new EMA guidance focuses on the design of study protocols and defining study populations.

In the European Union (EU), 40% of initial marketing authorization applications for products currently on the market contained RWE.

Publications
Published on:
July 15, 2022
Written by:
Romain Finas, Oriane Courbier
We believe the development of data models and platforms co-designed by scientific associations, industry stakeholders, and patients, represents a real opportunity to sustain France’s and Europe’s comparative advantages in a global competition for health data.
Romain Finas Vice President, Real-World Evidence

RWE can accelerate time to market — regulators are already suggesting that pharmaceutical companies use the data collection of a patient registry to gather information on clinical use and monitor the safety and effectiveness of authorized medicines when used in the real-world setting. For example, real-world data (RWD) as a synthetic control arm, helps compare longitudinal data of patients receiving new drugs with pre-existing patient data, offering an ethical solution for orphan drug trials. 

That’s why a new guideline to help design study protocols and define study populations has been published by the European Medicines Agency (EMA). Published on October 26, 2021, the report also provides guidance on data collection, data quality management, and data analysis to achieve higher quality evidence. For example, it states that the data collection method should be clearly described in the study protocol as it has implications regarding potential sources of bias and confounding, adequate retrieval of missing data, and safety reporting requirements. 

To see the real opportunity and challenges of French and European data models and platforms in a global competition environment for health data, it’s important to go beyond the guidance:  

Today, relevant sources, mixing genomics data, and electronic medical records are the gold standard for classifying populations according to their treatment responses and developing personalized medicine algorithms in oncology. To meet current and future research demands, data sources need to be continuously enriched, requiring increased agility in data collection and massive investments.   

  • Our recommendation: EU data source champions should not only focus on data generation but also on biobanks and sequencing capabilities. This offers more agility in biomarker testing incorporation and resequencing for quality monitoring.  

Beyond the technical or data platform standards, setting up common data models in collaboration with scientific associations, healthcare industries, and patient groups for each therapeutic area is essential. 

  • Our recommendation: EU data source champions need to research building a consensus into a minimum viable data codesigned by pharma patients and research. 

This approach is key to producing useful, standardized datasets for the federation of hospitals. Without the combined alignment of technical interoperability standards or data lake technology, along with a relevant dataset definition, we cannot achieve scalability of artificial intelligence (AI) innovations or population health management solutions in hospitals. 

  • Our recommendation: EU data source champions should clearly communicate the benefits of usage of healthcare data to patients, as well as the massive investment it takes to transform raw data into regulatory and scientific-grade sources. Patient onboarding is critical to avoid controversy on the monetization of personal data that is considered for the common good. 

The next hurdle to overcome is the automation of data quality and plausibility monitoring. This will be the major determinant in producing regulatory grade datasets. Data collection should now go beyond craftmanship—thanks to AI development in natural language processing.  

  • Our recommendation: EU data source champions need to allocate a significant part of the investment to run a cohort for AI training to “read” and “check” data plausibility.  

We believe the development of data models and platforms co-designed by scientific associations, industry stakeholders, and patients, represents a real opportunity to sustain France’s and Europe’s comparative advantages in a global competition for health data.

Empowering health institutions to base product access decisions on reliable data collected in an ethical manner is instrumental to attract research and offer patients earlier access to innovative treatment and healthcare solutions. 

Abbreviations 
  • AI: Artificial intelligence 
  • EMA: European Medicines Agency 
  • RWE: Real-World Evidence 
  • RWD: Real-world data 
References 

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