Real-world data (RWD) and real-world evidence (RWE) continue to positively impact drug development, from preclinical study to loss of exclusivity. However, RWE teams face challenges getting leadership buy-in, choosing the right questions, selecting fit-for-purpose data sources, and, ultimately, finding their place within small and large organizations. Innovative RWE methodologies, strategic data sourcing, tactical project selection, and well-implemented technology stacks can help new or expanding RWE teams navigate these challenges, scale, and integrate their groups more deeply into their organizations for maximum impact on R&D, clinical development, and beyond.
The size, structure, and legacy of a company or organization can affect how much impact your RWE team can have across the asset lifecycle and the broader organization. Larger companies or those that have been around a while may have RWE teams and processes integrated across many different therapeutic areas, a structure that likely took years to form.
By contrast, newer and smaller companies may not be as integrated and lack data management and IT infrastructure. For RWE teams in these situations, forging connections between related groups, such as statisticians, bioinformatics, data scientists, and medical affairs, and developing a shared language can help to integrate your department within the larger organization.
Whether your organization's RWE or epidemiology team has existed for decades, years, or months, the need to demonstrate value and contribute to the overall company mission is strong.
But how does your team choose a project that promises to have a significant, positive, lasting impact on the organization? And for teams not yet integrated into the organization, how do you connect with the colleagues you need and turn them into believers and advocates?
Early wins, such as using RWD/RWE to address critical internal business questions or resolve data-related challenges, can build advocacy and support within the organization. Focusing on a few vital initiatives and executing them successfully can establish credibility and provide a “quick win” that can lead to expanding RWE capabilities.
Here’s a real-life example: In the early 2000s, two publications (here and here), using data from the US Renal Data System, reported a correlation between 1-year mortality in hemodialysis patients and higher doses of a drug manufactured by Amgen, epoetin alfa. After a rigorous study, a 2008 publication from a team at Amgen recapitulated these findings and demonstrated that this correlation resulted from a biased analysis confounded by indication.
Looking for these types of successes helps to establish an RWE team's value, laying the foundation for long-term impact and trust.
Teams must also be strategic and focused on picking the right questions to drive impactful decision-making and the right data to support answering those questions.
Using published frameworks, such as SPIFD and SPACE, can help create organizational confidence in your RWE team’s ability to identify the correct data and questions, respectively. In short, the SPACE framework ensures that real-world studies are designed to be reliable and valuable, while SPIFD helps RWE teams ensure that their data sources are decision-grade and fit-for-purpose.
Once trust is built on smaller yet important initiatives, RWE methodologies and processes developed at the asset level can be scaled to address broader organization-wide questions “above the asset.” Establishing robust data analysis and decision-making methods, such as creating a “clean room” for blinded analysis, can support regulatory submissions and reduce internal and external concerns about data bias.
Building scalable RWE strategies that address project-specific and portfolio-wide needs can help organizations leverage real-world insights for internal decision-making and regulatory success, ultimately enhancing the overall impact of RWE efforts on your organization's drug development efforts and patient outcomes.
As your RWE efforts scale and answer broader questions across the organization, wider access to RWD across different teams may be requested. Data governance committees may be helpful in properly managing these requests and ensuring that the data is used to perform principal epidemiological studies according to established SOPs. These committees can also connect teams with the proper SME, well-versed in the details of a dataset, to act as a resource for their project.
Applying the right technology can make a major difference in meeting your short- and long-term goals.
No matter the size of your organization, project tracking tools can make a major difference for RWE teams: They help monitor the progress of multi-year projects and ensure compliance with internal and external regulatory bodies.
Knowledge management systems – for storing validated algorithms, study protocols, and cohort information – are also essential for retaining internal knowledge, particularly when personnel turnover occurs.
As with all new and existing tools, they can only “do good” for organizations that use them correctly and to their fullest extent. To ensure that happens, buy-in from all key stakeholders is necessary. Without that, new tools may be onboarded and not used properly, leading to more inefficiencies and less return on investment.
AI is the most recent addition to the technology stack of an RWE team, enabling faster and more efficient decision-making and a competitive advantage over those not using AI. These tools allow teams to analyze differences in findings across studies, helping identify key discrepancies in algorithms and methodologies.
When it comes to AI, collaboration and trust between data science, AI, and epidemiology teams are critical for successful implementation. Speaking a common language and building alignment ensures that all groups can effectively collaborate to deliver high-quality insights.
Carefully choosing the questions, data sources, and technology can greatly impact the role RWE and your team play in your organization. Innovative and robust RWE methodologies and processes can scale from addressing specific asset-level issues to broader portfolio-wide applications, ultimately supporting internal decision-making and bigger-ticket regulatory approvals.
To see how Aetion can help drive results for your drug development efforts, learn more about our software or contact us today.