A hierarchical testing approach for detecting safety signals in clinical trials.

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ID: 95050
2020
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Abstract
Detecting safety signals in clinical trial safety data is known to be challenging due to high dimensionality, rare occurrence, weak signal, and complex dependence. We propose a new hierarchical testing approach for analyzing safety data from a typical randomized clinical trial. This approach accounts for the hierarchical structure of adverse events (AEs), that is, AEs are categorized by system organ class (SOC). Our approach contains two steps: the first step tests, for each SOC, whether any AEs within this SOC are differently distributed between treatment arms; and the second step identifies signal AEs from SOCs passing the first step tests. We show the superiority, in terms of power of detecting safety signals given controlled false discovery rate, of the new approach comparing with currently available approaches through simulation studies. We also demonstrate this approach with two real data examples.
Reference Key
tan2020astatistics Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tan, Xianming;Chen, Bingshu E;Sun, Jianping;Patel, Tejendra;Ibrahim, Joseph G;
Journal Statistics in Medicine
Year 2020
DOI
10.1002/sim.8495
URL
Keywords
clinical trials signal detection drug safety mist hierarchical testing multiplicity

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