Evaluating the Long-term Efficacy of Coronavirus Disease 2019 (COVID-19) Vaccines

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Abstract

Large-scale deployment of safe and durably effective vaccines can curtail the coronavirus disease-2019 (COVID-19) pandemic. However, the high vaccine efficacy (VE) reported by ongoing phase 3 placebo-controlled clinical trials is based on a median follow-up time of only about 2 months, and thus does not pertain to long-term efficacy. To evaluate the duration of protection while allowing trial participants timely access to efficacious vaccine, investigators can sequentially cross participants over from the placebo arm to the vaccine arm. Here, we show how to estimate potentially time-varying placebo-controlled VE in this type of staggered vaccination of participants. In addition, we compare the performance of blinded and unblinded crossover designs in estimating long-term VE.

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  1. SciScore for 10.1101/2021.01.13.21249779: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationIn this scenario, participants were screened and randomly assigned to vaccine or placebo over a 4-month period, and the vaccine was approved in the 5th month on the basis of interim results.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.


    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.

    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.