From Data to Decisions: Lessons from 10+ Years in Clinical Biometrics

By Kristina Leus, Head of Biometrics, Cromos Pharma

Clinical trials today generate more data than ever before. Electronic data capture, decentralized study models, wearable devices, and real world data streams have significantly increased both scale and complexity.

At the same time, one principle remains unchanged. Clinical development does not succeed because of how much data is collected. It succeeds because of how well that data is structured, interpreted, and translated into evidence.

After more than a decade in clinical data management and biostatistics, several lessons consistently stand out.

Clean Data Is Not the End Goal

Database lock is often treated as a key milestone. In reality, it marks the beginning of the most critical phase, interpretation. A dataset can be technically complete but still create challenges if the structure was not designed with final endpoints in mind.

Case: In a Phase II oncology study, the eCRF was built before the statistical analysis plan was finalized. The primary endpoint required time to event analysis, but visit windows were inconsistent across patients. This led to two weeks of post lock derivations and repeated regulatory queries. In a later study, alignment between eCRF and SAP reduced the time from database lock to analysis to three days.

Takeaway: Statistical thinking should guide data collection from the start, not after the database is locked.

Integration Across Biometrics Is a Strategic Advantage

Clinical trials rely on close coordination between data management, statistical programming, and biostatistics. When these functions operate in silos, inefficiencies accumulate and timelines become less predictable.

Case: In a rare disease study, a protocol amendment introduced an additional visit. In a siloed setup, this resulted in a three week delay after the statistical model no longer worked as expected. In an integrated team, the impact was assessed jointly and the updated database was delivered within five days without breaking the model.

Takeaway: Early collaboration across biometrics functions reduces risk and improves execution speed.

Speed Matters, but Architecture Matters More

Sponsors increasingly expect faster timelines, but speed without consistency creates downstream complexity, especially in later phases and during submission.

Case: In one development program, Phase I studies used different variable names for the same parameter. By Phase III, data pooling required four weeks of manual remapping and introduced submission risks. In a later program with consistent naming from the beginning, pooling took only two hours.

Takeaway: Speed drives progress, but structure ensures scalability and reliability.

Innovation Must Be Balanced with Oversight

Artificial intelligence and automation are increasingly used in data workflows, particularly for anomaly detection and data reconciliation. These tools can significantly improve efficiency, but they cannot replace expert judgment.

Case: In a cardiovascular trial, an automated tool identified abnormal ECG patterns within days. However, each signal was reviewed by a biostatistician before queries were issued, preventing false positives and unnecessary site burden.

Takeaway: Technology accelerates processes, while expert oversight protects data quality and credibility.

Data Complexity Is Increasing

Modern trials incorporate decentralized elements and continuous data streams, which introduce new challenges in data handling and analysis. Missing data patterns are often no longer random and must be addressed proactively.

Case: In a decentralized pain study, wearable devices generated continuous activity data. Missing values occurred during sleep and device removal. A predefined rule for handling gaps was established before database lock and accepted by regulators without questions.

Takeaway: More data requires stronger planning and predefined analytical rules, not reactive corrections.

Stability of Teams Still Matters

Despite rapid technological progress, team experience and continuity remain critical success factors in biometrics delivery.

Case: A biometrics team working together across multiple studies reduced derivation errors by 40 percent and resolved queries twice as fast compared to a newly assembled team working on a similar protocol.

Takeaway: Stable teams improve efficiency, consistency, and overall study quality.

Final Thought

At the end of every clinical study, the central question remains the same. Can the data support a clear and defensible conclusion.

Biometrics plays a critical role in answering this question by ensuring that data is analysis ready, transparent, and reliable.

As clinical trials continue to evolve, the importance of strong biometrics will only increase. At Cromos Pharma, this is supported by an integrated approach that brings together data management and biostatistics into a unified process, enabling consistent, high quality, decision ready evidence.