Predicting the Future: Why Forecasting is the Missing Piece in Clinical Trial Management

When we look at how clinical trials are managed today, we see a critical gap. The industry is excellent at describing what happened yesterday but far less equipped to predict what will happen tomorrow. This asymmetry isn't just an academic concern—it directly impacts trial quality, timelines, and outcomes.

We're Looking Backward, Not Forward

In our analysis of statistical approaches in Risk-Based Quality Management, we've found approximately 85% of efforts focus on what's already occurred: 50% univariate analysis and 35% bivariate or relational approaches. Only 10% of statistical applications look forward using predictive methods.

This imbalance means the industry is essentially driving while looking through the rearview mirror. We need to shift our collective gaze toward what's coming next.

Completing the D2P Cycle

At Veranex, we approach trial analytics through what we call the "D2P" framework:

1. Descriptive: What happened
2. Diagnostic: Why did it happen
3. Predictive: What will happen next
4. Prescriptive: What should we do about it?

Most trial teams excel at the first two but struggle with the last two. As we often tell our partners, "The bigger question is what are we going to do with those identified risks." Identification without action provides little value.

The Art of Asking Predictive Questions

Effective forecasting begins with framing the right questions. For enrollment, we might ask: "What is the probability of completing targeted enrollment within our timeline?" For compliance: "What's the likelihood a site will report more than three major deviations next quarter?"

These questions transform abstract data into actionable intelligence. We've found that restructuring our inquiries toward probability and future outcomes automatically shifts our mindset from reactive to proactive.

Case Study: Enrollment Prediction in Action

In one recent study, we analyzed data from 84 active sites, though only 31 were actually enrolling subjects. Our predictive model indicated:

• Target enrollment would be reached in approximately 8.7 months
• 18 additional sites would need activation at current enrollment rates

This gave the sponsor three clear options: activate new sites, motivate currently inactive sites, or increase enrollment rates at active locations. Instead of vague concerns about "slow enrollment," they had specific, quantifiable actions to consider.

From Statistical Probability to Strategic Action

In another case study, we predicted subject elimination from the per-protocol population. We found a 43% probability that more than 17 subjects would be eliminated—critical information for planning and risk mitigation.

The value isn't in the math itself but in translating probability into action. When we present a 43% risk, we're not just sharing numbers—we're providing the foundation for strategic decisions about subject retention strategies.

The Future of RBQM

While predictive analytics represents a smaller percentage of RBQM applications today, we believe its role will grow substantially. The technology and methodologies exist now—we don't need to wait for future innovations.

Our experience has shown that whenever significant data is available, complementing traditional cumulative and current risk management with predictive approaches completes the risk management cycle. This completion isn't just theoretical—it delivers tangible improvements in trial quality, efficiency, and decision-making.

As we continue to refine our approach to clinical trial quality at Veranex, we encourage all stakeholders to look beyond what has happened to what could happen. That shift in perspective might be the difference between a successful trial and a failed one.