Statistics and the detection of scientific misconduct

Abstract

The paper by Bolland et al.1 in this issue had a longer “gestation” than usual at Neurology ®; it was received on December 4, 2015. We usually review on a tight timeline and publish important work quickly. But this manuscript has a different focus as it presents a statistical analysis that demonstrates probable scientific misconduct (fraud) on a large scale. It is also unique in that its analysis uses complicated methods that may be beyond what most readers are willing to tackle. However, the bottom line is simple: there are statistical methods that, when properly applied, can detect fraudulent behavior by investigators. Clinical trial data can thereby be validated or called into question. In this instance, analysis of a collection of studies by a large research group suggested that at least some of the studies were likely to be fraudulent.

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