Region of attainable redaction, an extension of ellipse of insignificance analysis for gauging impacts of data redaction in dichotomous outcome trials
Abstract
In biomedical science, it is a reality that many published results do not withstand deeper investigation, and there is growing concern over a replicability crisis in science. Recently, Ellipse of Insignificance (EOI) analysis was introduced as a tool to allow researchers to gauge the robustness of reported results in dichotomous outcome design trials, giving precise deterministic values for the degree of miscoding between events and non-events tolerable simultaneously in both control and experimental arms1 (Grimes 2022). While this is useful for situations where potential miscoding might transpire, it does not account for situations where apparently significant findings might result from accidental or deliberate data redaction in either the control or experimental arms of an experiment, or from missing data or systematic redaction. To address these scenarios, we introduce Region of Attainable Redaction (ROAR), a tool that extends EOI analysis to account for situations of potential data redaction. This produces a bounded cubic curve rather than an ellipse, and we outline how this can be used to identify potential redaction through an approach analogous to EOI. Applications are illustrated, and source code including a web-based implementation that performs EOI and ROAR analysis in tandem for dichotomous outcome trials is provided.
Data availability
All data is available in the paper and at github: https://github.com/drg85/EOIROAR_codeWeb implementation is available at: https://drg85.shinyapps.io/EOIROAR/
Article and author information
Author details
Funding
Wellcome Trust (214461/A/18/Z)
- David Robert Grimes
Wellcome Trust (214461/A/18/Z)
- David Robert Grimes
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2024, Grimes
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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