Instead, we can use multimodal RWD to better understand how patients perform on the current standard of care, and better select patients that are not benefiting from the current standard of care but may benefit from an experimental drug. This not only reduces cost, but – critically – helps increase the study’s probability of success to bring needed therapies to patients faster.

Using multimodal RWD, it is possible to more accurately estimate performance at each step in the clinical trial planning process. Rather than basing assumptions on dated literature studies, RWD can help researchers estimate the performance of the control group, determine the optimal patient selection strategies, and understand the treatment landscape for current patients.

Top oncology pharma companies are using this approach and testing it in a subset of their studies to see if using data, instead of literature, can improve a trial’s results, and, if so, by how much. When using multimodal RWD, the teams are typically able to validate their assumptions, understand patients’ performance, and gain insights on the right patient selection strategies. The return on investment can be in the hundreds of millions of dollars, which significantly validates the hypothesis and proves the financial value of a data-based approach to trial design.

In phase 3, RWD should be incorporated from the earliest planning stages to inform site selection and study design. Heterogeneous data sources can also help researchers understand the performance of the current standard of care. Post-registration, researchers can use data to generate evidence that could be published in congresses or at conferences to communicate the value of a treatment to payers.

The insights gained from designing registration phase 3 studies are typically not published, and become intellectual property of the company that they can use to inform future strategies and studies.

The second step is understanding the data. There is no such thing as a perfect dataset; all of them are different, so it is important to understand the nuances and biases inherent in each.

Third, it’s critical to work side-by-side with global product teams, who have a thorough understanding of the asset and deep clinical expertise. There needs to be a symbiotic relationship between data and product experts in which they can share ideas and work together on the same team. That’s where the magic happens and ensures the best possible outcome.