By Tom Dougherty (Director of RWE Partnerships & Innovation, Pfizer) and Ryan Moog (Head of Trials, Datavant)
Datavant recently concluded Real-World Data Connect in New York, an event geared towards bringing togetherLife Sciences organizations operating at the vanguard of real-world data exploration and application. One track of the event focused on clinical development use cases for real-world data and provided industry pioneers a forum to share how they’re working to advance the future of clinical development, driving innovation and contributing to the improvement of patient care. While this was a relatively small, invitation-only event, we collected several takeaways that are worthwhile to share with any organization considering the incorporation of real-world data into their clinical trials:
Lessons Learned
Design Considerations + Timing
The decision on when to tokenize trials and engage with linked real-world data (RWD) emerged as a significant design consideration. Optimal timing is crucial in maximizing data utility. Some sponsors find immediate benefits in incorporating linked RWD even into interim analyses, while others prefer to reserve tokens for potential downstream analysis as new research questions arise. The decision is dependent on trial-specific objectives, emphasizing the flexible nature of tokenization and RWD in clinical research.
Furthermore, the application of trial tokenization technology warrants attention to the consenting process. Informed consent is a cornerstone of trials; however, the requirement for explicit patient consent may depend on the study’s objective and nature. Sponsors need to adhere strictly to ethical and regulatory guidelines when bypassing explicit consent, ensuring the balance between research requirements and patient rights is maintained.
Internal Stakeholder Engagement
Engaging both internal and external stakeholders is crucial when using RWD in clinical trials. It is essential to foster collaboration between Health Economics and Outcomes Research (HEOR), integrated evidence teams and trialists to create awareness of the opportunities and considerations required in the use of real-world data. Consistently across companies, we’ve heard clinical development and clinical operations teams are still maturing in their awareness and use of RWD, but eager to learn. Equally important is to educate stakeholders who are new to working with data, such as legal and operations teams. Addressing concerns related to patient buy-in, biospecimen sensitivity, and potential impacts on trial recruitment, can pave the way for smoother implementation. Panelists at Real-World Data Connect reported that, if possible, this cross-stakeholder engagement should happen early and often and ideally more than 60 days in advance of study kickoff.
Education of Clinical Sites
It is essential to underline the importance of educating clinical sites about clinical trial tokenization. This technology replaces sensitive patient information with non-sensitive data, allowing a comprehensive understanding of their clinical history while simultaneously ensuring privacy. Clinical sites, being the primary touchpoints for patients, need to be well-versed in explaining this process. Their ability to communicate effectively about tokenization can reassure patients about data security, and highlight the advantages of having a more complete, yet anonymized, representation of their clinical history within the trial. This enhanced understanding can potentially improve patient engagement and retention in the study, ultimately contributing to more successful and representative clinical trials.
Focus on Bias and Data Quality
A pivotal aspect forLife Sciences organizations employing RWD in clinical trials is the focus on reducing bias and enhancing data quality. RWD brings a plethora of information from diverse sources, and while it adds richness to clinical trial data, it also introduces complexities, including potential bias and data variability. It is crucial for teams to fully understand the target research questions RWD can answer when linked to clinical trials to help support selection of RWD, as there is a wide range of data collection, management, and curation methods used in creating real-world datasets. Biases may stem from various factors like underreporting, selective reporting, or data entry errors. Increasing data quality involves rigorous data cleaning, validation processes, and cross-referencing with other reliable sources. A robust approach towards bias reduction and data quality enhancement can significantly improve the reliability, utility, and impact of RWD in clinical trials.
Opportunities
Ancillary Studies and RWD Sub-Studies7
The use of linked RWD via clinical trial tokenization opens a new frontier in clinical research, allowing for the creation of sub-studies within a primary clinical trial. Linked RWD, which includes diverse healthcare information from various sources, can be merged with these anonymized trial data to create comprehensive patient profiles. Researchers can then use these enriched data sets to conduct sub-studies focusing on specific aspects of the trial, such as examining secondary endpoints, exploring subgroup effects, or investigating adverse events. This approach not only leverages the wealth of RWD but also adds depth and breadth to the clinical trial, potentially leading to richer insights and more robust conclusions. These studies can reveal crucial insights that can further validate trial results, unveil unexpected correlations, or guide future research directions.
Post-Trial Considerations
Leveraging linked RWD after the conclusion of a clinical trial can be crucial in addressing emerging safety signals. Post-trial surveillance can provide invaluable insights into long-term effects or rare adverse events that might not have surfaced during the controlled environment of a clinical trial. RWD offers an expansive view of patient experiences in diverse real-world settings, enhancing the ability to identify and understand these safety signals. Given the critical importance of this task, robust mechanisms need to be in place to continuously monitor and manage these events. Timely identification and management of emerging safety signals not only ensures patient safety but also contributes to the overall credibility of the trial findings, and informs the safe and effective use of the therapy in the broader population. Depending on study design and post-marketing surveillance needs, insights from these extended observation periods could also augment or even replace expensive existing models for data collection via patient registries.
The use of RWD in clinical trials offers an immense opportunity to accelerate medical research and enhance patient care. As the industry continues to evolve, fostering collaboration, promoting operational efficiency, focusing on data quality, and ensuring robust post-trial mechanisms will be key to successful RWD integration. The future is exciting, and the path forward is illuminated by the insights gained from events such as Real-World Data Connect that focus on shared learnings across the RWD community.
Please remember, the implementation of RWD in trials is a journey, and as we step into this new frontier, let’s continue learning and adapting for the benefit of global health.
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