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Diagnosing fraudulent baseline data in clinical trials
The first table in many articles reporting results of a randomized clinical trial compares baseline factors across arms. Results that appear inconsistent with chance trigger suspicion, and in one case, accusation and confirmation of data falsification. We confirm theoretically results of simulation...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527254/ https://www.ncbi.nlm.nih.gov/pubmed/32998158 http://dx.doi.org/10.1371/journal.pone.0239121 |
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author | Proschan, Michael A. Shaw, Pamela A. |
author_facet | Proschan, Michael A. Shaw, Pamela A. |
author_sort | Proschan, Michael A. |
collection | PubMed |
description | The first table in many articles reporting results of a randomized clinical trial compares baseline factors across arms. Results that appear inconsistent with chance trigger suspicion, and in one case, accusation and confirmation of data falsification. We confirm theoretically results of simulation analyses showing that inconsistency with chance is extremely difficult to prove in the absence of any information about correlations between baseline covariates. We offer a reasonable diagnostic to trigger further investigation. |
format | Online Article Text |
id | pubmed-7527254 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-75272542020-10-02 Diagnosing fraudulent baseline data in clinical trials Proschan, Michael A. Shaw, Pamela A. PLoS One Research Article The first table in many articles reporting results of a randomized clinical trial compares baseline factors across arms. Results that appear inconsistent with chance trigger suspicion, and in one case, accusation and confirmation of data falsification. We confirm theoretically results of simulation analyses showing that inconsistency with chance is extremely difficult to prove in the absence of any information about correlations between baseline covariates. We offer a reasonable diagnostic to trigger further investigation. Public Library of Science 2020-09-30 /pmc/articles/PMC7527254/ /pubmed/32998158 http://dx.doi.org/10.1371/journal.pone.0239121 Text en https://creativecommons.org/publicdomain/zero/1.0/ This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication. |
spellingShingle | Research Article Proschan, Michael A. Shaw, Pamela A. Diagnosing fraudulent baseline data in clinical trials |
title | Diagnosing fraudulent baseline data in clinical trials |
title_full | Diagnosing fraudulent baseline data in clinical trials |
title_fullStr | Diagnosing fraudulent baseline data in clinical trials |
title_full_unstemmed | Diagnosing fraudulent baseline data in clinical trials |
title_short | Diagnosing fraudulent baseline data in clinical trials |
title_sort | diagnosing fraudulent baseline data in clinical trials |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7527254/ https://www.ncbi.nlm.nih.gov/pubmed/32998158 http://dx.doi.org/10.1371/journal.pone.0239121 |
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