Operational Challenges of Using Overall Survival as a Primary Endpoint

In a recent video interview with Applied Clinical Trials, Ananth Kadambi, VP of real-world evidence and modeling solutions at Certara, outlined the operational hurdles sponsors face as the FDA increasingly requires overall survival (OS) as the primary endpoint in oncology studies. He emphasized the importance of pre-specified safety monitoring, analytical subgroup planning, and strategies to minimize patient dropouts. This regulatory shift places equal weight on demonstrating efficacy and safety, with implications for post-approval monitoring and patient access. Kadambi noted that model-informed drug development and real-world evidence can support more efficient trial designs. For clinical operations teams, the focus will be on strengthening patient tracking and engaging data monitoring committees earlier in the process to balance trial efficiency with regulatory and payer demands in complex oncology indications.

ACT: What are the key operational challenges sponsors should anticipate as the FDA begins requiring overall survival as the primary endpoint in randomized oncology trials?

Kadambi: The main thrust of the guidance really is to bring to the forefront the fact that the assessment of harm or the safety risk is a consideration that should be kept on the same level as efficacy. Operationally speaking, particularly in indications that have a short natural history, sponsors are really going to be needing to consider making overall survival the primary endpoint in their in their trials. This might be a bit problematic in indications with longer natural history, so in those cases, they'll need to consider alternative approaches, whether they be long-term extension studies, real-world evidence based observational studies, or other mechanisms in order to capture that information.

I think the other piece that's important here is that the agency is requiring pre specified safety monitoring, specification of analytical subgroups, and statistical analyzes, so there will definitely need to be some pre-planning involved that's probably above and beyond what normally happens in the context of most trials. I think operationally speaking, also, there will be an increased focus on minimizing the number of dropouts in a study and being able to track all of the patients that are enrolled because every patient counts as far as getting an adequate signal goes, particularly as far as safety endpoints are concerned.