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Common Data Elements (CDEs) are crucial for data standardization. This study found that only a small fraction of defined CDEs are truly common across real-world datasets, highlighting challenges in data discoverability and reuse.

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Area of Science:

  • Biomedical Informatics
  • Data Science
  • Genomics

Background:

  • Common Data Elements (CDEs) standardize data collection across studies.
  • Prospective CDE development by experts is traditional.
  • Limited research exists on real-world CDE usage and impact on data discoverability.

Purpose of the Study:

  • To investigate the prevalence of CDEs in real-world datasets.
  • To assess the impact of varying commonness thresholds on CDE identification.
  • To evaluate post-hoc mapping benefits and challenges.

Main Methods:

  • Analysis of mapped variables from 426 studies in the Database of Phenotypes and Genotypes (dbGaP).
  • Application of varying commonness thresholds to identify CDEs.
  • Semantic grouping of PhenX variables collected in at least 15 studies.

Main Results:

  • Only 1,414 out of 24,938 defined PhenX variables were observed in the analyzed dbGaP studies.
  • With a commonness threshold of n=15, 32 out of 68 semantically grouped PhenX variables were identified as truly common.
  • Demonstrated a low observed frequency of defined CDEs in practice.

Conclusions:

  • Post-hoc mapping of study data to CDE frameworks enhances data findability and reuse.
  • Significant informatics challenges exist in integrating Electronic Health Record data with research CDEs.
  • Systematic research is needed to bridge the gap between CDE definition and real-world application.