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Controlling false discovery proportion in identification of drug‐related adverse events from multiple system organ classes
Author(s) -
Tan Xianming,
Liu Guanghan F.,
Zeng Donglin,
Wang William,
Diao Guoqing,
Heyse Joseph F.,
Ibrahim Joseph G.
Publication year - 2019
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.8304
Subject(s) - false discovery rate , multiple comparisons problem , permutation (music) , computer science , identification (biology) , event (particle physics) , statistical hypothesis testing , type i and type ii errors , data mining , statistics , medicine , mathematics , biochemistry , chemistry , physics , botany , quantum mechanics , biology , acoustics , gene
Analyzing safety data from clinical trials to detect safety signals worth further examination involves testing multiple hypotheses, one for each observed adverse event (AE) type. There exists certain hierarchical structure for these hypotheses due to the classification of the AEs into system organ classes, and these AEs are also likely correlated. Many approaches have been proposed to identify safety signals under the multiple testing framework and tried to achieve control of false discovery rate (FDR). The FDR control concerns the expectation of the false discovery proportion (FDP). In practice, the control of the actual random variable FDP could be more relevant and has recently drawn much attention. In this paper, we proposed a two‐stage procedure for safety signal detection with direct control of FDP, through a permutation‐based approach for screening groups of AEs and a permutation‐based approach of constructing simultaneous upper bounds for false discovery proportion. Our simulation studies showed that this new approach has controlled FDP. We demonstrate our approach using data sets derived from a drug clinical trial.