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Efficient methods for signal detection from correlated adverse events in clinical trials.

Authors
  • Diao, Guoqing1
  • Liu, Guanghan F2
  • Zeng, Donglin3
  • Wang, William2
  • Tan, Xianming3
  • Heyse, Joseph F2
  • Ibrahim, Joseph G3
  • 1 Department of Statistics, George Mason University, Fairfax, Virginia.
  • 2 Merck & Co., Inc., North Wales, Pennsylvania.
  • 3 Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
Type
Published Article
Journal
Biometrics
Publisher
Wiley (Blackwell Publishing)
Publication Date
Sep 01, 2019
Volume
75
Issue
3
Pages
1000–1008
Identifiers
DOI: 10.1111/biom.13031
PMID: 30690717
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

It is an important and yet challenging task to identify true signals from many adverse events that may be reported during the course of a clinical trial. One unique feature of drug safety data from clinical trials, unlike data from post-marketing spontaneous reporting, is that many types of adverse events are reported by only very few patients leading to rare events. Due to the limited study size, the p-values of testing whether the rate is higher in the treatment group across all types of adverse events are in general not uniformly distributed under the null hypothesis that there is no difference between the treatment group and the placebo group. A consequence is that typically fewer than 100 α percent of the hypotheses are rejected under the null at the nominal significance level of α . The other challenge is multiplicity control. Adverse events from the same body system may be correlated. There may also be correlations between adverse events from different body systems. To tackle these challenging issues, we develop Monte-Carlo-based methods for the signal identification from patient-reported adverse events in clinical trials. The proposed methodologies account for the rare events and arbitrary correlation structures among adverse events within and/or between body systems. Extensive simulation studies demonstrate that the proposed method can accurately control the family-wise error rate and is more powerful than existing methods under many practical situations. Application to two real examples is provided. © 2019 International Biometric Society.

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