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Beyond demographic tables: integrating data quality in clinical trial representativeness
João Gregório1, Agnieszka Lemanska2, Bartlomiej Cieszynski3
1Informatics, Data Science Department, National Physical Laboratory, Glasgow, United Kingdom.
Frontiers in Digital Health
|May 25, 2026
Summary
This study introduces a quantitative framework to assess clinical trial representativeness by measuring demographic coverage and dataset completeness. The framework provides a suitability score, enhancing trial design and regulatory evaluation.
Area of Science:
- Clinical Trials
- Health Informatics
- Biostatistics
Background:
- Clinical trial representativeness ensures findings generalize to target populations.
- Current subjective methods lack standardization and data quality assessment.
- Existing frameworks don't address cohort suitability for representativeness analysis.
Purpose of the Study:
- Propose a quantitative framework for clinical trial representativeness.
- Integrate demographic coverage and dataset completeness into a unified evaluation.
- Advance systematic, quantifiable metrics for trial quality assessment.
Main Methods:
- Developed a framework measuring demographic coverage (Jensen-Shannon Distance) and dataset completeness (data availability).
- Aggregated metrics using clinically informed weights into a single suitability score.
- Validated using simulated cohorts from MIMIC-III against a target sepsis population.
Main Results:
- Demographic alignment significantly drives overall suitability.
- Coverage and completeness vary independently, validating the composite metric.
- Suitability scores ranged from 86.5% to 91.6%; cohort size did not correlate with suitability.
Conclusions:
- The framework offers interpretable and reproducible metrics for trial design.
- Supports evidence-based enrollment strategies and transparent regulatory evaluation.
- Establishes a foundation for systematic evaluation of clinical trial representativeness.
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