Considerations for the use of real-world evidence in non-interventional studies: Comments for the FDA

Originally posted on the PicnicHealth Life Science blog.

Earlier this year, the FDA published a guidance document titled “Real-World Evidence: Considerations Regarding Non-Interventional Studies for Drug and Biological Products”, that provides study design and analysis recommendations to sponsors looking to use real-world data (RWD) in observational research. This guidance, which emphasizes the potential for real-world evidence (RWE) to meet regulatory standards for evidence generation, underscores the FDA's recognition of RWE as a valuable component in drug and biological product evaluation.

This guidance is very helpful in providing clarity on how to use RWD and RWE and how it can be used for regulatory decision making. However, based on the latest market trends and advancements in data collection, we suggest a few areas where it would be beneficial for the FDA to expand their guidance around the use of RWE. Below, we share four of our recommendations.  

Embracing AI and Machine Learning in RWE Studies

With the rapid advancement of artificial intelligence (AI) and the critical role it can play in observational studies, we suggest further guidance on the use of AI in collecting data and generating RWE. Given the transformative potential of AI/ML in data interpretation and analysis, it is crucial for the FDA to address how these technologies can be evaluated and validated. This includes transparency in the use of AI throughout study design, conduct, and reporting, ensuring quality metrics are established to address bias and model drift.

Broadening the Definition of RWD

The volume and types of RWD have been growing, so it will be important to expand the definition of RWD to include a wider range of data types, such as primary data collected from patients, caregivers, and healthcare providers. This broader definition will better accommodate current and future data sources.

Addressing Novel Data Sources

As data collection methods evolve, it is essential to maintain flexibility in the guidance for incorporating various data sources beyond traditional claims and electronic health records (EHRs). With new direct-to-patient approaches, it is now easier to collect primary data from patients and caregivers than ever before for observational research. Because of this, it will be helpful for life science companies to have guidance from the FDA on how to provide transparency in data curation, particularly when utilizing innovative approaches. This will support the integration of diverse data types and enhance the robustness of real-world evidence.

Harmonizing with Global Standards

We recommend the FDA align its guidance with global standards, particularly the European Medicines Agency’s (EMA) draft guidance on real-world data. Harmonizing principles will facilitate consistent study designs and methodologies, promoting international collaboration and data comparability. Additionally, incorporating recommendations from ICH E8(R1) and the upcoming ICH E6(R3) will ensure that observational studies meet high-quality standards and are integrated into the broader framework of good clinical practices.

The latest FDA guidance provides great insights on the role and use of RWE in observational studies, and we are excited to see greater clarity on this topic. By incorporating some of these recommendations, the FDA can further enhance the flexibility, transparency, and global alignment of its guidance, ultimately improving the quality and acceptance of real-world evidence in regulatory decision-making.

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