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A method for analyzing commonalities in clinical trial target populations.
Zhe He1, Simona Carini2, Tianyong Hao1
1Department of Biomedical Informatics, Columbia University, New York, NY.
Summary
This study introduces COMPACT, a database analyzing clinical trial eligibility criteria to identify commonalities and biases in target populations. This facilitates knowledge reuse for future trial design and research subgroup selection.
Area of Science:
- Clinical informatics
- Biomedical data science
- Clinical trial methodology
Background:
- ClinicalTrials.gov offers opportunities to analyze target population commonalities.
- Understanding these commonalities aids knowledge reuse in designing future clinical trial eligibility criteria.
- It also helps reveal potential systematic biases in selecting population subgroups for clinical research.
Purpose of the Study:
- To present a novel data resource for analyzing commonalities in clinical trial target populations.
- To facilitate knowledge reuse and identify potential biases in clinical research participant selection.
Main Methods:
- Developed a two-part method: parsing and indexing eligibility criteria text, and mining common eligibility features and attributes.
- Created the "Commonalities in Target Populations of Clinical Trials" (COMPACT) database to store structured eligibility criteria and trial metadata.
- Illustrated the use of COMPACT with an analytic module, CONECT, using Type 2 diabetes trials as an example.
Main Results:
- The COMPACT database provides a structured format for eligibility criteria and trial metadata.
- The CONECT module, powered by COMPACT, demonstrates the analysis of commonalities in target populations for Type 2 diabetes trials.
- Analyzed 4,493 clinical trials for Type 2 diabetes to identify population commonalities.
Conclusions:
- The COMPACT database is a valuable resource for analyzing clinical trial target population commonalities.
- This approach enables better knowledge reuse in eligibility criteria design.
- Facilitates the identification of systematic biases in clinical research participant selection.
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