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Flagging clinical adverse experiences: reducing false discoveries without materially compromising power for detecting true signals
Author(s) -
Mehrotra Devan V.,
Adewale Adeniyi J.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5310
Subject(s) - flagging , false positive paradox , false positives and false negatives , computer science , false discovery rate , tolerability , adverse effect , medicine , statistics , artificial intelligence , mathematics , archaeology , gene , history , biochemistry , chemistry
Comparative analyses of safety/tolerability data from a typical phase III randomized clinical trial generate multiple p ‐values associated with adverse experiences (AEs) across several body systems. A common approach is to ‘flag’ any AE with a p ‐value less than or equal to 0.05, ignoring the multiplicity problem. Despite the fact that this approach can result in excessive false discoveries (false positives), many researchers avoid a multiplicity adjustment to curtail the risk of missing true safety signals. We propose a new flagging mechanism that significantly lowers the false discovery rate (FDR) without materially compromising the power for detecting true signals, relative to the common no‐adjustment approach. Our simple two‐step procedure is an enhancement of the Mehrotra–Heyse–Tukey approach that leverages the natural grouping of AEs by body systems. We use simulations to show that, on the basis of FDR and power, our procedure is an attractive alternative to the following: (i) the no‐adjustment approach; (ii) a one‐step FDR approach that ignores the grouping of AEs by body systems; and (iii) a recently proposed two‐step FDR approach for much larger‐scale settings such as genome‐wide association studies. We use three clinical trial examples for illustration. Copyright © 2012 John Wiley & Sons, Ltd.