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Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

Author

Listed:
  • Christian Thiele

    (University of Applied Sciences Bielefeld)

  • Gerrit Hirschfeld

    (University of Applied Sciences Bielefeld)

  • Ruth Brachel

    (Ruhr-University Bochum)

Abstract

Registries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

Suggested Citation

  • Christian Thiele & Gerrit Hirschfeld & Ruth Brachel, 2021. "Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9733-9750, December.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:12:d:10.1007_s11192-021-04111-w
    DOI: 10.1007/s11192-021-04111-w
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    References listed on IDEAS

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    1. Jinseok Kim & Jenna Kim, 2018. "The impact of imbalanced training data on machine learning for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 511-526, October.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Carolina Riveros & Agnes Dechartres & Elodie Perrodeau & Romana Haneef & Isabelle Boutron & Philippe Ravaud, 2013. "Timing and Completeness of Trial Results Posted at ClinicalTrials.gov and Published in Journals," PLOS Medicine, Public Library of Science, vol. 10(12), pages 1-12, December.
    4. Jens Peter Andersen & Björn Hammarfelt, 2011. "Price revisited: on the growth of dissertations in eight research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 371-383, August.
    5. Grolemund, Garrett & Wickham, Hadley, 2011. "Dates and Times Made Easy with lubridate," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 40(i03).
    6. Mike Thelwall & Kayvan Kousha, 2016. "Are citations from clinical trials evidence of higher impact research? An analysis of ClinicalTrials.gov," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 1341-1351, November.
    7. Mehmet Ali Abdulhayoglu & Bart Thijs, 2018. "Use of locality sensitive hashing (LSH) algorithm to match Web of Science and Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 1229-1245, August.
    8. Darren B Taichman & Peush Sahni & Anja Pinborg & Larry Peiperl & Christine Laine & Astrid James & Sung-Tae Hong & Abraham Haileamlak & Laragh Gollogly & Fiona Godlee & Frank A Frizelle & Fernando Flor, 2017. "Data Sharing Statements for Clinical Trials: A Requirement of the International Committee of Medical Journal Editors," PLOS Medicine, Public Library of Science, vol. 14(6), pages 1-3, June.
    Full references (including those not matched with items on IDEAS)

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