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. 2012;7(3):e33677.
doi: 10.1371/journal.pone.0033677. Epub 2012 Mar 16.

The database for aggregate analysis of ClinicalTrials.gov (AACT) and subsequent regrouping by clinical specialty

Asba Tasneem  1 , Laura Aberle, Hari Ananth, Swati Chakraborty, Karen Chiswell, Brian J McCourt, Ricardo Pietrobon
Affiliations

The database for aggregate analysis of ClinicalTrials.gov (AACT) and subsequent regrouping by clinical specialty

Asba Tasneem et al. PLoS One. 2012.

Abstract

Background: The ClinicalTrials.gov registry provides information regarding characteristics of past, current, and planned clinical studies to patients, clinicians, and researchers; in addition, registry data are available for bulk download. However, issues related to data structure, nomenclature, and changes in data collection over time present challenges to the aggregate analysis and interpretation of these data in general and to the analysis of trials according to clinical specialty in particular. Improving usability of these data could enhance the utility of ClinicalTrials.gov as a research resource.

Methods/principal results: The purpose of our project was twofold. First, we sought to extend the usability of ClinicalTrials.gov for research purposes by developing a database for aggregate analysis of ClinicalTrials.gov (AACT) that contains data from the 96,346 clinical trials registered as of September 27, 2010. Second, we developed and validated a methodology for annotating studies by clinical specialty, using a custom taxonomy employing Medical Subject Heading (MeSH) terms applied by an NLM algorithm, as well as MeSH terms and other disease condition terms provided by study sponsors. Clinical specialists reviewed and annotated MeSH and non-MeSH disease condition terms, and an algorithm was created to classify studies into clinical specialties based on both MeSH and non-MeSH annotations. False positives and false negatives were evaluated by comparing algorithmic classification with manual classification for three specialties.

Conclusions/significance: The resulting AACT database features study design attributes parsed into discrete fields, integrated metadata, and an integrated MeSH thesaurus, and is available for download as Oracle extracts (.dmp file and text format). This publicly-accessible dataset will facilitate analysis of studies and permit detailed characterization and analysis of the U.S. clinical trials enterprise as a whole. In addition, the methodology we present for creating specialty datasets may facilitate other efforts to analyze studies by specialty groups.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. A schematic representation of the database for Aggregate Analysis of ClinicalTrials.Gov (AACT) with its key enhancements.
Figure 2
Figure 2. High-level Entity-Relationship Diagram (ERD) for AACT.
Figure 3
Figure 3. Percentage of interventional studies with complete data by registration year for selected data elements.
Figure 4
Figure 4. An overview of methodology and process of developing clinical specialty datasets.
The INTERVENTIONS, CONDITIONS, and KEYWORDS tables consist of disease condition terms provided by data submitters that include both MeSH and non-MeSH terms. The INTERVENTION_BROWSE and CONDITION_BROWSE tables are populated by MeSH terms generated by NLM algorithm (a) Process illustrating how MeSH terms are created in ClinicalTrials.gov. Tables and data shown here does not represent entire ClinicalTrials.gov database (b) Process illustrating the annotation and validation of disease conditions (c) Process illustrating the creation of specialty datasets.
Figure 5
Figure 5. MeSH trees for acromegaly.
Source: 2010 online MeSH thesaurus (available: http://www.nlm.nih.gov/cgi/mesh/2010/MB_cgi).
Figure 6
Figure 6. Rules for deciding whether a given study belongs to a given specialty.
See this image and copyright information in PMC

References

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Publication types

MeSH terms