Designing an evidence-based Bayesian network for estimating the risk versus benefits of AstraZeneca COVID-19 vaccine

  1. Helen J. Mayfield
  2. Colleen L. Lau
  3. Jane E. Sinclair
  4. Samuel J. Brown
  5. Andrew Baird
  6. John Litt
  7. Aapeli Vuorinen
  8. Kirsty R. Short
  9. Michael Waller
  10. Kerrie Mengersen

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Abstract

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  1. Version published to 10.1016/j.vaccine.2022.04.004
  2. ScreenIT

    SciScore for 10.1101/2021.10.28.21265588: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    Six subject matter experts with experience in virology (KRS, JES), clinical practice (AB, CLL, JL), biostatistics (MW, KM), and infectious disease epidemiology (CLL, JL) were involved in gathering and interpreting available information.
    AB
    suggested: RRID:BDSC_203)
    Software and Algorithms
    SentencesResources
    The most influential nodes were identified using the ‘Sensitivity Analysis’ function in the GeNie modeller software program (Bayes Fusion 2019).
    GeNie
    suggested: (GENIE, RRID:SCR_009197)

    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.

  3. Version published to 10.1101/2021.10.28.21265588 on medRxiv