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Publications 2014, 2(1), 27-37; doi:10.3390/publications2010027

A Case-Control Comparison of Retracted and Non-Retracted Clinical Trials: Can Retraction Be Predicted?

1
Medical Communications Consultants LLC, 103 Van Doren Place, Chapel Hill, NC 27517, USA
2
Departments of Psychiatry, Biostatistics, and Psychology, University of North Carolina, Chapel Hill, NC 27517, USA
*
Author to whom correspondence should be addressed.
Received: 7 September 2013 / Revised: 9 October 2013 / Accepted: 10 October 2013 / Published: 27 January 2014
(This article belongs to the Special Issue Misconduct in Scientific Publishing)
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Abstract

Does scientific misconduct severe enough to result in retraction disclose itself with warning signs? We test a hypothesis that variables in the results section of randomized clinical trials (RCTs) are associated with retraction, even without access to raw data. We evaluated all English-language RCTs retracted from the PubMed database prior to 2011. Two controls were selected for each case, matching publication journal, volume, issue, and page as closely as possible. Number of authors, subjects enrolled, patients at risk, and patients treated were tallied in cases and controls. Among case RCTs, 17.5% had ≤2 authors, while 6.3% of control RCTs had ≤2 authors. Logistic regression shows that having few authors is associated with retraction (p < 0.03), although the number of subjects enrolled, patients at risk, or treated patients is not. However, none of the variables singly, nor all of the variables combined, can reliably predict retraction, perhaps because retraction is such a rare event. Exploratory analysis suggests that retraction rate varies by medical field (p < 0.001). Although retraction cannot be predicted on the basis of the variables evaluated, concern is warranted when there are few authors, enrolled subjects, patients at risk, or treated patients. Ironically, these features urge caution in evaluating any RCT, since they identify studies that are statistically weaker.
Keywords: data fabrication; data falsification; scientific fraud; randomized clinical trial data fabrication; data falsification; scientific fraud; randomized clinical trial
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Steen, R.G.; Hamer, R.M. A Case-Control Comparison of Retracted and Non-Retracted Clinical Trials: Can Retraction Be Predicted? Publications 2014, 2, 27-37.

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