Obstacles to the reuse of study metadata in ClinicalTrials.gov

Abstract

Objective: ClinicalTrials.gov is a registry of clinical-trial metadata whose use is required by many funding agencies and scientific publishers. Metadata are essential to the reuse of data, but issues such as heterogenous metadata schemas, inconsistent values, and usage of free text instead of controlled terms pervade many metadata repositories. Our objective is to evaluate the quality of metadata about clinical studies in ClinicalTrials.gov and to document strategies to improve metadata accuracy. Methods: Using 302,091 metadata records, we evaluated whether values adhere to type expectations for Boolean, integer, date, age, and value-set fields, and whether records contain fields required by the Food and Drug Administration. We tested whether values for condition and intervention use terms from biomedical ontologies, and whether values for eligibility criteria follow the recommended format. Results: For simple fields, records contain correctly typed values, but there are anomalies in value-set fields. Contact information, outcome measures, and study design are frequently missing or underspecified. Important fields for search, such as condition and intervention, are not restricted to ontology terms, and almost half of the values for condition are not from MeSH, as recommended. Eligibility criteria are stored as unstructured free text. Conclusions: ClinicalTrials.gov's data-entry system enforces a schema with type restrictions, freeing records from common issues in metadata repositories. However, lack of ontology restrictions or structure for the condition, intervention, and eligibility criteria elements significantly impairs reusability. Searchability of the database depends on infrastructure that maps free-text values to terms from UMLS ontologies.Metadata are the lifeblood of biomedical data. At the simplest level, metadata are data that describe other data. In practice, we expect metadata to be structured and standardized, and to be useful in making the underlying data findable and reusable. High-quality metadata enhance scientific reproducibility and transparency, allow researchers to pool studies to increase the statistical power of inferences,[1] and enable the use of "big data" machine learning techniques. International metadata repositories such as the National Center for Biotechnology Information's (NCBI) BioSample and the European Bioinformatics Institute's (EBI) BioSamples repositories encourage data reuse through the availability of comprehensive metadata. They each gather metadata from several different repositories of biological data into a centralized, searchable database. Ideally, they also ensure that metadata follow unified standards and schema regardless of the author, source, and format of the original data.Unfortunately, biomedical metadata are plagued by numerous quality issues. Hu et al. examined the quality of the metadata that accompany data records in the Gene Expression Omnibus (GEO) and found that they suffered from type inconsistency (e.g., numerical fields populated with non-numerica...

Obstacles to the reuse of study metadata in ClinicalTrials.gov

“…The data used and generated throughout the study described in this paper are available in Figshare at https:// doi.org/10.6084/m9.figshare.12743939 71 . Stanford University's PRS test environment was used for qualitative analysis of the PRS system.…”

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