The Quiet Work of Data Cleaning in Decentralized Trials

It does not make headlines. It does not show up on dashboards. But data cleaning is where the quality of a trial is often defined.

In decentralised studies with inputs from apps, wearables, remote visits, and cloud-based forms... the job of reviewing, validating, and reconciling data becomes more complex. And more important.

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Where the Mess Comes From

Even the best-designed study platform generates noise. Common issues include:

• Participants skipping entries
• Sites duplicating visit dates
• Timestamp mismatches due to time zones
• Contradictory data across systems (e.g. self-report vs. device)

This is not error in the traditional sense. It is signal mixed with context... and cleaning is how you make it usable.

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The Real Role of a Data Manager

Modern data managers are not just chasing blanks. They are:

• Reviewing outliers for plausibility
• Flagging protocol deviations missed in real time
• Clarifying free-text fields with ambiguous responses
• Mapping interim database versions to ensure consistency

Their decisions shape the final dataset, and affect both statistical validity and regulatory acceptance.

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What Good Cleaning Looks Like

1. Planned logic: Cleaning should start with pre-defined rules. What counts as an outlier? When is a value re-coded? What fields need manual review?
2. Layered review: Not all data gets equal attention. Critical endpoints receive full review. Supporting fields may be checked through scripts or samples.
3. Documented decisions: Every correction or clarification should be tracked. Not just what changed, but why.
4. Cross-functional input: Sometimes, a CRA sees a pattern before the data team does. Or a PI’s note resolves a systemic issue. Collaboration matters.

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Automation Helps - But Doesn’t Replace Judgment

Automated queries and range checks are useful. But they do not catch:

• Mislabelled events
• Plausible but incorrect sequences
• Coded values with inconsistent logic

A human still has to look at the whole story. Tools support that -they do not replace it.

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Clean Data = Trustworthy Outcomes

The goal is not perfection. It is reliability. Regulators, funders, and participants want to know the data reflects what actually happened, and that the team behind it took care to make sure it does.

That care often happens quietly. But it is the backbone of every strong study.

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