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Using systematic data categorisation to quantify the types of data collected in clinical trials: the DataCat project
Evelyn Crowley1, Shaun Treweek2, Katie Banister3
1Health Research Board Clinical Research Facility, University of Cork, Cork, Ireland.
Trials
|June 18, 2020
Summary
Clinical trials collect substantial data unrelated to primary outcomes. Secondary outcomes and non-outcome data represent the majority of collected information, highlighting inefficiencies in clinical trial data collection.
Area of Science:
- Clinical research methodology
- Health services research
Background:
- Clinical trial data collection is resource-intensive, involving significant time, effort, and cost.
- The volume of data collected impacts trial staff and participant burden.
- Understanding data proportions is crucial for efficient resource allocation and participant engagement.
Purpose of the Study:
- To categorize data types collected in clinical trials.
- To determine the proportion of data represented by each category.
Main Methods:
- Developed a standard operating procedure for data categorization (primary outcome, secondary outcome, other).
- Categorized variables from 18 randomized superiority trials (investigational medicinal products and complex interventions).
- Employed independent paired categorization with protocol review and team consultation for disagreements.
Main Results:
- Primary outcome data constituted a small fraction: 5.0% (median) / 11.2% (mean).
- Secondary outcome data represented a larger proportion: 39.9% (median) / 42.5% (mean).
- Non-outcome data (e.g., identifiers, demographics) comprised 32.4% (median) / 36.5% (mean).
Conclusions:
- A significant portion of clinical trial data is not related to primary outcomes.
- Secondary outcomes generate considerably more data than primary outcomes.
- Researchers should prioritize essential data collection to align with trial objectives and inform healthcare decisions.
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