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External Control Arms

Build smarter control groups with fit-for-purpose real-world patient cohorts.

Shorter timelines. Stronger evidence.

Based on a proven methodology built upon 30+ years of experience, TriNetX external control arms (ECAs) help you with timely, representative, granular, and fit-for-purpose control groups.

Designed for studies where randomized controls are impractical, our experts will guide you through set-up, regulations, and execution as we use real-world patient data in lieu of additional patient recruitment, reducing costs and timelines while generating robust, regulation-ready evidence to support your trial.

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Navigate regulatory uncertainty with confidence

Strengthen your HTA and EMA submissions with representative, fit-for-purpose ECAs designed for regulatory readiness.

Our methodology ensures scientifically sound data and processes, delivering evidence that regulators can trust and increasing your chances of a successful submission.

Overcome data and implementation challenges

TriNetX ECAs maintain the integrity of your analyses and results while helping you gain deeper insights into treatment impact. You can even generate supplementary control information for prospective experimental arms.

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Want to learn more?

Explore our insights

The IBD Evidence Shift: Five Real-World Studies That Change What Life Sciences Teams Know
Blogs

The evidence environment for inflammatory bowel disease has changed. Life Sciences teams are drawing on real-world data generated at a scale and resolution that trials were never designed to match.

Our Approach to Data Quality
eBooks

Learn how TriNetX applies a shared, research-community framework to make real-world data research-ready. From source to study, quality you can verify.

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Blogs

When RWE findings fail under scrutiny, the conversation almost always turns first to data quality, but that is rarely where the real problem begins.

The Next Generation of Clinical AI is Here. Is Your Data Foundation Ready?
Blogs

The next generation of clinical AI is here. Is your data foundation ready? As AI grows more proactive, gaps in data comprehensiveness, quality, recency, and transparency matter more.

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