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.
journal_name
Stat Medjournal_title
Statistics in medicineauthors
Tan X,Chen BE,Sun J,Patel T,Ibrahim JGdoi
10.1002/sim.8495subject
Has Abstractpub_date
2020-05-15 00:00:00pages
1541-1557issue
10eissn
0277-6715issn
1097-0258journal_volume
39pub_type
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