Emerging Therapies for COVID-19: The Value of Information From More Clinical Trials

  1. Stijntje W. Dijk
  2. Eline M. Krijkamp
  3. Natalia Kunst
  4. Cary P. Gross
  5. John B. Wong
  6. M.G. Myriam Hunink

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  1. Version published to 10.1016/j.jval.2022.03.016
  2. ScreenIT

    SciScore for 10.1101/2022.03.29.22273041: (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

    Software and Algorithms
    SentencesResources
    26 Detailed information on all parameters is included in the Excel supplementary file.
    Excel
    suggested: None

    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: To the best of our knowledge, our study is the first to perform a VOI analysis for the treatment of COVID-19 patients. Whereas trials and meta-analyses often conclude further research is needed7, and where current guidelines are based on statistical significance in clinical evidence, VOI results are based on both uncertainty as well as the potential consequences of making decisions with and without further evidence. Our paper expands the potential impact of drug approval by investigating not only drug efficacy and effectiveness, but also the overall net benefits. Considering the unprecedented rollout of clinical trials investigating potential treatments for COVID-19, objective research prioritization seems paramount.3,5 Our model can be updated with further evidence from trials and (cumulative) meta-analyses as they become available to continuously evaluate the optimal overall strategy as the pandemic evolves. Input parameters were based on best-available evidence. Some parameters, such as the quality of life of COVID-19 patients in 5 years and the costs of research, had to be estimated based on previous studies considering other diseases. Where necessary, we chose conservative approaches to our model parameters and settings. For example, treatment effects were only applied for reported trial duration and not extrapolated beyond and wide distributions were chosen to represent large uncertainty. Additionally, we calculated the net value for the US po...

    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 10.1101/2022.03.29.22273041v1 on medRxiv