Decision-Making in Early-Phase Oncology Trials: Navigating Uncertainty with Evidence Frameworks

Early‑phase oncology development pushes sponsors to make high‑stakes decisions with small patient numbers, evolving biology, and incomplete evidence. In this Citeline‑hosted webinar, experts from clinical development, regulatory science, and statistics explored how structured decision architectures and evidence frameworks can help organizations navigate uncertainty while keeping patient impact at the center of decision‑making.

Throughout the discussion featuring Andrew Krivoshik, MD, PhD, PE (Chief Medical Officer, PAQ Therapeutics), Mark Stewart, PhD (Vice President of Science Policy, Friends of Cancer Research), Aiden Flynn, PhD (Senior Vice President, Strategic Statistical Services, MMS), and Ben Dudley (Chief Commercial Officer, MMS), the panel emphasized that while uncertainty is an inevitable aspect of early-phase oncology, poor decision-making does not have to be. The session highlighted several key themes and actionable insights that can help guide organizations toward more confident and informed choices in the face of uncertainty.

The Right Data at the Right Decision Points

Early‑phase oncology programs move quickly, often with rapid decision cycles and limited data. The panel stressed that while speed is critical, the focus should be on ensuring that the right data is available at each decision point. In early oncology, complete datasets are rarely available; sponsors should identify the critical information required to make informed go/no‑go decisions, so emerging signals can be interpreted and enacted with confidence. Speakers also underscored that not all decisions are binary; well‑designed frameworks allow for deliberate pauses or targeted data collection when signals are promising but incomplete.

Small datasets, heterogeneous patient populations, evolving biology, and immature endpoints create multiple sources of uncertainty in early oncology trials. The goal, as discussed, is not to eliminate uncertainty, but to make it interpretable. Structured evidence frameworks, early planning, and predefined decision rules help organizations understand what the data is and is not telling them.

Interpreting Early Signals: Context, Endpoints, and Patient Impact

With randomized comparators often unavailable, the panel stressed interpreting early efficacy and safety signals in context, using external benchmarks (e.g., historical controls and real-world data) and fit-for-purpose endpoints to make single-arm results more meaningful.

• Interim overall survival can be informative, but it should sit within the totality of evidence, often most useful for spotting early signals of harm rather than proving benefit.
• Decision frameworks should be built with the end in mind (e.g., accelerated approval paths), aligning design and evidence plans to future regulatory expectations.
• Across all choices the patient impact should remain the anchor, alongside differentiation in a competitive oncology landscape.

Upfront Quantitative Planning: Statistics, Modeling, and Simulation

Speakers emphasized strengthening confidence at key inflection points by defining targets, analyses, and decision thresholds early supported by statistical input and scenario-based modeling.

• Predefine success criteria, targets, and analyses for major decisions (e.g., proof-of-concept) to improve transparency with regulators, partners, and investors.
• Treat “not meeting criteria” as a valid outcome that enables timely resource reallocation.
• Use modeling and simulation to stress-test thresholds, compare development scenarios, and prioritize tumor types or combinations before (and during) execution.

In summary, effective interpretation of early signals and rigorous quantitative planning are essential for driving meaningful progress in oncology research. By leveraging external benchmarks, tailoring endpoints, and maintaining patient impact as a central focus, clinical programs can generate results that resonate with both regulatory bodies and the wider healthcare community. Upfront statistical planning and scenario modeling further enhance decision-making, ensuring that resources are directed toward the most promising avenues. Together, these strategies foster transparency, adaptability, and innovation, ultimately advancing therapies that offer real benefits to patients.

Watch the on-demand webinar to learn more: Early-Phase Oncology Trials: Decision Making Webinar