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

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.

Registration quality and availability of publications for clinical trials in Germany and the influence of structural factors

“…A single trial may be registered on multiple registries, and while ClinicalTrials.gov and the DRKS are among the most important registries for German trials, there are numerous other registries contained in the ICTRP. A full breakdown of the source registries has already been reported previously [ 22 ]. The percentages for all of these factors were similar in the sample that was drawn for manual inspection.…”

“…We used the pipe-delimited files for AACT, the CSV-export function for DRKS, and the full export file in CSV format for the ICTRP. Further details are given in a previous article [ 22 ].…”

Estimating the effect of COVID-19 on trial design characteristics: a registered report

“…Addressing the limitations related to cross-registration would have required substantial manual work which was not feasible. Emerging computational methods are being explored to aid in easing the manual burden of trial matching in future analyses [63].…”

The international clinical trials registry platform (ICTRP): data integrity and the trends in clinical trials, diseases, and drugs

“…This difference is largely due to the varying standards in the initial registries. Standardization of fields like specified conditions and interventions, as well as improvements in duplicate identification, could significantly enhance the platform ( van Valkenhoef et al, 2016 ; Kumari et al, 2020 ; Saberwal, 2021 ; Thiele et al, 2021 ).…”

“…In a recent review from 2020, a model was developed to identify true pairs by comparing common entries across all clinical trial databases, such as scientific and public titles, phases, conditions, and outcome measures, using a string-match method ( Kumari et al, 2020 ). In 2021, another group of researchers used several methods, including a random forest classifier and decision trees, to improve the deduplication process and increase the precision and accuracy of predictions compared to regular study ID matching ( Thiele et al, 2021 ). Despite these efforts, several authors emphasize the need for further development of methods and research into the deduplication process ( van Valkenhoef et al, 2016 ; Kumari et al, 2020 ; Saberwal, 2021 ).…”