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Before any real insights can be gleaned from a trials’ results, all collected data needs to be cleaned, verified and signed off on. But while the drug modalities and trial designs have seen incredible innovation over the last few decades, the way the clinical research industry reviews and cleans clinical trial data has not changed much.
Anyone who has been involved with clinical data review will recognize this cycle: build edit checks, wait for SAS listings, analyze monthly reports, and issue queries weeks after the data was first entered. And it works, sort of. These checks catch missing values, out-of-range numbers, and date inconsistencies. But they also leave behind a long tail of errors that only surface late in the trial, or worse, during database lock crunch time.
Data managers are the unsung heroes of the clinical trial process. They build a foundational level of hygiene into the study database, enabling all downstream analysis. They own the delivery of that final clean database, managing the challenges of protocol variability, site performance, and cross-functional responsibilities throughout the study. It's a thankless job candidly, and as studies get more complex and data volumes increase, the burden on data managers will only continue to grow.
Hear me out. The average phase 3 clinical trial today involves 3.5 million data points collected (up from 1 million in 2012). As protocols become more complex, the burden on study teams to collect, clean and analyze data continues to worsen, leading to mistakes, delays and rework.
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