Results availability and timeliness of registered COVID-19 clinical trials: interim cross-sectional results from the DIRECCT study

  1. Maia Salholz-Hillel
  2. Peter Grabitz
  3. Molly Pugh-Jones
  4. Daniel Strech
  5. Nicholas J DeVito

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Abstract

To examine how and when the results of COVID-19 clinical trials are disseminated.

Design

Cross-sectional study.

Setting

The COVID-19 clinical trial landscape.

Participants

285 registered interventional clinical trials for the treatment and prevention of COVID-19 completed by 30 June 2020.

Main outcome measures

Overall reporting and reporting by dissemination route (ie, by journal article, preprint or results on a registry); time to reporting by dissemination route.

Results

Following automated and manual searches of the COVID-19 literature, we located 41 trials (14%) with results spread across 47 individual results publications published by 15 August 2020. The most common dissemination route was preprints (n=25) followed by journal articles (n=18), and results on a registry (n=2). Of these, four trials were available as both a preprint and journal publication. The cumulative incidence of any reporting surpassed 20% at 119 days from completion. Sensitivity analyses using alternate dates and definitions of results did not appreciably change the reporting percentage. Expanding minimum follow-up time to 3 months increased the overall reporting percentage to 19%.

Conclusion

COVID-19 trials completed during the first 6 months of the pandemic did not consistently yield rapid results in the literature or on clinical trial registries. Our findings suggest that the COVID-19 response may be seeing quicker results disclosure compared with non-emergency conditions. Issues with the reliability and timeliness of trial registration data may impact our estimates. Ensuring registry data are accurate should be a priority for the research community during a pandemic. Data collection is underway for the next phase of the DIssemination of REgistered COVID-19 Clinical Trials study expanding both our trial population and follow-up time.

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  1. Version published to 10.1136/bmjopen-2021-053096
  2. Version published to 10.1101/2021.04.07.21255071v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.04.07.21255071: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Software, Code, and Data: ICTRP and registry data collection, management, and parts of the analysis were performed in Python 3.8.1
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and Limitations: This work had a number of strengths. We used a broad definition of interventional clinical trials, beyond just randomized controlled trials, and included the full ICTRP database in our potential population. Searches for publications were performed using multiple strategies and databases, with dual-coded manual verification throughout. Our findings were also robust to a number of alternate assumptions for judging completion and results availability. This work also has limitations. Missing, incomplete, inaccurate, or outdated data all likely impacted our sample to varying degrees that are difficult to quantify. Deviations from best practices for discoverability (e.g., inclusion of registration IDs in abstracts and metadata), publications in non-indexed journals or preprint servers, and articles not available in English language may have made some results difficult to locate despite our extensive search strategy. Lastly, our methods may under-represent some aspects of how results have been shared during the pandemic. For instance, large adaptive trials like the RECOVERY trial will essentially contain numerous trials under one broad registration, produce numerous publications, and will only be represented as a single datapoint in our analysis that remains ongoing until the end of follow-up for the final arm. Research in Context: Registered and reported COVID-19 trials have been well-described in the literature.[7,21–25] Our results add to these examinat...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2021.04.07.21255071v1 on medRxiv