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Diagnosing fraudulent baseline data in clinical trials

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  • Michael A Proschan
  • Pamela A Shaw

Abstract

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.

Suggested Citation

  • Michael A Proschan & Pamela A Shaw, 2020. "Diagnosing fraudulent baseline data in clinical trials," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-12, September.
    • Handle: RePEc:plo:pone00:0239121
    DOI: 10.1371/journal.pone.0239121
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    References listed on IDEAS

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    1. Rebecca A Betensky & Sy Han Chiou, 2017. "Correlation among baseline variables yields non-uniformity of p-values," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-7, September.
    2. Martin Bland, 2013. "Do Baseline P-Values Follow a Uniform Distribution in Randomised Trials?," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-5, October.
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