Hypothesis-Agnostic Network-Based Analysis of Real-World Data Suggests Ondansetron is Associated with Lower COVID-19 Any Cause Mortality

  1. Gregory M. Miller
  2. J. Austin Ellis
  3. Rangaprasad Sarangarajan
  4. Amay Parikh
  5. Leonardo O. Rodrigues
  6. Can Bruce
  7. Nischal Mahaveer Chand
  8. Steven R. Smith
  9. Kris Richardson
  10. Raymond Vazquez
  11. Michael A. Kiebish
  12. Chandran Haneesh
  13. Elder Granger
  14. Judy Holtz
  15. Jacob Hinkle
  16. Niven R. Narain
  17. Bret Goodpaster
  18. Jeremy C. Smith
  19. Daniel S. Lupu

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Abstract

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  1. Version published to 10.1007/s40801-022-00303-9
  2. Version published to 10.1101/2021.10.05.21264578v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.10.05.21264578: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: This study was approved by the AdventHealth Institutional Review Board (#1590483).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisTo increase the statistical power, imputation of missing values was accomplished by a multiple imputation approach using the predictive mean matching method [9], as implemented by the R package mice [10].

    Table 2: Resources

    Antibodies
    SentencesResources
    Cohort selection: The RECOVER-19 registry, which is continuing to collect data, included 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods from January to December 2020.
    antigen ,
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.10.05.21264578v1 on medRxiv