De-Risking Development: Prioritizing Robustness in Trial Design

Biopharma sponsors are conditioned to chase two metrics above all: speed and cost.In the race to market, every effort is poured into shortening trial durations and optimizing sample size.

When applied across a global portfolio, a 20% to 40% reduction in costs or time is extremely significant. But if you zoom in on a single, make-or-break trial, especially for a biotech or focused pharma team, the stakes are different.

The priority shifts from improving efficiency to ensuring success. When success is mission critical and missing that primary endpoint means the whole program stops, saving a percentage of costs or cutting a few dozen patients is not the priority.

The budget is already set. The goal is not to marginally improve statistical power, but to fundamentally de-risk the program to secure a positive outcome.

The Unforeseen Threat: Uncertainty in Drug Development

The core challenge in drug development is the inherent uncertainty, much of which remains uncharacterized until the program is actually underway. Clinical trials are subject to multiple real-world variables, including varying site competence, patient data variability, changes in the standard of care and fundamental uncertainties about the drug's effect size.

When success is paramount, the solution is to actively design the trial to handle this unpredictability. This is precisely where robustness in clinical trial design becomes essential.

The Hidden Risk of Conservative Trial Design

Biopharma sponsors often select sites or designs based on past experiences that suggest a high probability of success. However, if those experiences do not account for underlying uncertainty, the entire program is potentially exposed to unnecessary risk.

The danger extends beyond operational challenges such as site underperformance to the risk that initial assumptions about effect size or patient response were misaligned with reality from the start.

In traditional fixed trials, the study design is locked prior to the enrollment of the first patient, with parameters such as sample size and primary endpoints remaining unchanged. However, if the observed treatment effect differs from the baseline assumptions, this rigid approach can lead to trial failure.

This is not because the therapy lacks potential, but because the design lacks the mechanisms to adapt to emerging data. In a trial setting, robustness means more than managing operational variability.

It is the ability to maintain scientific integrity and hit targets even when real-world outcomes deviate from the protocol’s assumptions. By modeling a wide range of scenarios during the planning phase, sponsors can build flexibility into their designs to ensure the trial remains viable even when the biological or clinical reality shifts away from early estimates.

Adaptive Design: Turning Robustness into Action

With advanced analytics, AI, and machine learning, this robustness is no longer just theory. We can now incorporate it directly into the trial protocol using adaptive designs. These practical tools move away from a fixed plan and instead adapt to the signals the data sends.

• Sample Size Re-estimation (SSR): This is a powerful safety net. If the mid-trial data suggests the treatment effect is different from your initial hypothesis, SSR lets you pre-plan an adjustment to the patient numbers needed to maintain high statistical power. It stops you from running an underpowered study that's likely to fail or an unnecessarily bloated one.
• Enrichment Designs: In cases for which there is potential for substantial treatment heterogeneity, an enrichment design lets you take an early look at the data to focus on the most responsive subpopulation, such as patients with a specific biomarker. You can then focus all subsequent recruitment solely on that group, significantly improving the chance of a positive outcome.
• Group Sequential Designs: These allow for pre-planned checkpoints to stop a trial early if the drug is clearly working or clearly failing. Stopping for futility saves millions in wasted resources, while in multiarm trials, it allows you to drop less effective candidates and focus all resources on the best performers.

Beyond the design, if enrollment slows due to seasonal effects or difficulty finding a niche patient group, a robust design activates a contingency plan that dynamically adjusts site selection based on real-time enrollment data. It ensures you still hit your required statistical power targets, even when the initial forecasts are off.

Focus on What Matters

Efficiency matters, and sponsors will keep pursuing it. But when it comes to the success of a single program, maximizing the probability of a positive outcome is the ultimate priority.

For effective clinical development in 2026 and beyond, we must leverage advanced analytical methods, including sophisticated simulation and design optimization before a trial begins, all the way through to real-time monitoring.

By anticipating and actively designing for the unexpected, sponsors can de-risk their programs and significantly speed up the delivery of promising new therapies to the patients who need them.

About the Author

Elad Berkman is co-founder and CTO of PhaseV, which specializes in causal machine learning (ML) for clinical development. A trained physicist turned data scientist, Berkman has led dozens of operations research studies. He previously served as VP data science at transit technology company Via, leading the company's machine learning and data science efforts. Berkman has authored numerous patents at the intersection of ML and transportation.