Unreported links between trial registrations and published articles were identified using document similarity measures in a cross-sectional analysis of ClinicalTrials.gov

Adam G. Dunn, Enrico Coiera, Florence T. Bourgeois

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Objectives: Trial registries can be used to measure reporting biases and support systematic reviews, but 45% of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed.

Study Design and Setting: We extracted terms and concepts from a data set of 72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles and tested methods for ranking articles across 16,005 reported links and 90 manually identified unreported links. Performance was measured by the median rank of matching articles and the proportion of unreported links that could be found by screening ranked candidate articles in order.

Results: The best-performing concept-based representation produced a median rank of 3 (interquartile range [IQR] 1–21) for reported links and 3 (IQR 1–19) for the manually identified unreported links, and term-based representations produced a median rank of 2 (1–20) for reported links and 2 (IQR 1–12) in unreported links. The matching article was ranked first for 40% of registrations, and screening 50 candidate articles per registration identified 86% of the unreported links.

Conclusion: Leveraging the growth in the corpus of reported links between ClinicalTrials.gov and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles.

LanguageEnglish
Pages94-101
Number of pages8
JournalJournal of Clinical Epidemiology
Volume95
DOIs
Publication statusPublished - 1 Mar 2018

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PubMed
Cross-Sectional Studies
Registries
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Keywords

  • bibliographic database
  • clinical trial registry
  • clinical trial reporting
  • publication bias
  • reporting bias
  • systematic review
  • trial registration

Cite this

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title = "Unreported links between trial registrations and published articles were identified using document similarity measures in a cross-sectional analysis of ClinicalTrials.gov",
abstract = "Objectives: Trial registries can be used to measure reporting biases and support systematic reviews, but 45{\%} of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed. Study Design and Setting: We extracted terms and concepts from a data set of 72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles and tested methods for ranking articles across 16,005 reported links and 90 manually identified unreported links. Performance was measured by the median rank of matching articles and the proportion of unreported links that could be found by screening ranked candidate articles in order. Results: The best-performing concept-based representation produced a median rank of 3 (interquartile range [IQR] 1–21) for reported links and 3 (IQR 1–19) for the manually identified unreported links, and term-based representations produced a median rank of 2 (1–20) for reported links and 2 (IQR 1–12) in unreported links. The matching article was ranked first for 40{\%} of registrations, and screening 50 candidate articles per registration identified 86{\%} of the unreported links. Conclusion: Leveraging the growth in the corpus of reported links between ClinicalTrials.gov and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles.",
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N2 - Objectives: Trial registries can be used to measure reporting biases and support systematic reviews, but 45% of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed. Study Design and Setting: We extracted terms and concepts from a data set of 72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles and tested methods for ranking articles across 16,005 reported links and 90 manually identified unreported links. Performance was measured by the median rank of matching articles and the proportion of unreported links that could be found by screening ranked candidate articles in order. Results: The best-performing concept-based representation produced a median rank of 3 (interquartile range [IQR] 1–21) for reported links and 3 (IQR 1–19) for the manually identified unreported links, and term-based representations produced a median rank of 2 (1–20) for reported links and 2 (IQR 1–12) in unreported links. The matching article was ranked first for 40% of registrations, and screening 50 candidate articles per registration identified 86% of the unreported links. Conclusion: Leveraging the growth in the corpus of reported links between ClinicalTrials.gov and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles.

AB - Objectives: Trial registries can be used to measure reporting biases and support systematic reviews, but 45% of registrations do not provide a link to the article reporting on the trial. We evaluated the use of document similarity methods to identify unreported links between ClinicalTrials.gov and PubMed. Study Design and Setting: We extracted terms and concepts from a data set of 72,469 ClinicalTrials.gov registrations and 276,307 PubMed articles and tested methods for ranking articles across 16,005 reported links and 90 manually identified unreported links. Performance was measured by the median rank of matching articles and the proportion of unreported links that could be found by screening ranked candidate articles in order. Results: The best-performing concept-based representation produced a median rank of 3 (interquartile range [IQR] 1–21) for reported links and 3 (IQR 1–19) for the manually identified unreported links, and term-based representations produced a median rank of 2 (1–20) for reported links and 2 (IQR 1–12) in unreported links. The matching article was ranked first for 40% of registrations, and screening 50 candidate articles per registration identified 86% of the unreported links. Conclusion: Leveraging the growth in the corpus of reported links between ClinicalTrials.gov and PubMed, we found that document similarity methods can assist in the identification of unreported links between trial registrations and corresponding articles.

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