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Assessing the scientific integrity of the collected work of one author or author group

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dc.contributor.authorNielsen, J.
dc.contributor.authorBordewijk, E.M.
dc.contributor.authorGurrin, L.C.
dc.contributor.authorShivantha, S.
dc.contributor.authorFlanagan, M.
dc.contributor.authorLiu, S.
dc.contributor.authorLinn, M.M.
dc.contributor.authorZhou, K.X.
dc.contributor.authorvan Eekelen, R.
dc.contributor.authorBrown, N.J.L.
dc.contributor.authorThornton, J.
dc.contributor.authorMol, B.W.
dc.date.issued2025
dc.description.abstractObjectives: No published methods for research integrity review include both statistical techniques applied to groups of randomized trials and individual assessment of papers. We propose a method based on practical experience of investigating data integrity across the collected papers of an author or author group. Study Design and Setting: We report our approach to investigating the collected papers of an author or author group suspected of academic misconduct. Results: In the investigation of the work of an author or author group, we recommend a systematic search for the work of the involved authors in PubMed, Google Scholar, and the Retraction Watch database, as well as a search of trial registries for unpublished clinical trials. Summary information from studies should be tabulated to assess consistency between study registration, execution, and publication. Each paper should be investigated for unfeasible features of the governance, methodology, execution, results, and reporting of the study. Pairwise comparison of baseline and outcome tables between papers may reveal data duplication or unfeasibly large differences between baseline characteristics in similar studies. Assessment of baseline characteristics from multiple randomized trials using Carlisle’s method can determine whether the data are consistent with a properly executed randomization process, as can checking whether reported baseline characteristics follow expected patterns for random variables such as Benford’s law. If serious concerns are raised, a more thorough investigation should be performed by journals, publishers, and institutions. Conclusion: These methods provide a systematic and reproducible way to assess the collected work of an author or group of authors.
dc.description.statementofresponsibilityJeremy Nielsena, Esmée M. Bordewijka, Lyle C. Gurrinc, Siddharth Shivanthad, Madeline Flanagana, Sue Liud, May M. Linna, Kelly X. Zhoua, Rik van Eekelenb, Nicholas J.L. Browne, Jim Thorntonf, Ben W. Mol
dc.identifier.citationJournal of Clinical Epidemiology, 2025; 180:111603-1-111603-8
dc.identifier.doi10.1016/j.jclinepi.2024.111603
dc.identifier.issn0895-4356
dc.identifier.issn1878-5921
dc.identifier.orcidMol, B.W. [0000-0001-6887-0262] [0000-0001-8337-550X]
dc.identifier.urihttps://hdl.handle.net/2440/147175
dc.language.isoen
dc.publisherElsevier
dc.relation.granthttp://purl.org/au-research/grants/nhmrc/GNT1082548
dc.rights© 2025 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
dc.source.urihttps://doi.org/10.1016/j.jclinepi.2024.111603
dc.subjectData integrity; Research integrity; Academic misconduct; Randomization; Methods; Systematic review
dc.subject.meshHumans
dc.subject.meshResearch Design
dc.subject.meshScientific Misconduct
dc.subject.meshAuthorship
dc.subject.meshPublishing
dc.subject.meshRandomized Controlled Trials as Topic
dc.titleAssessing the scientific integrity of the collected work of one author or author group
dc.typeJournal article
pubs.publication-statusPublished

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