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

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 Med

journal_title

Statistics in medicine

authors

Tan X,Chen BE,Sun J,Patel T,Ibrahim JG

doi

10.1002/sim.8495

subject

Has Abstract

pub_date

2020-05-15 00:00:00

pages

1541-1557

issue

10

eissn

0277-6715

issn

1097-0258

journal_volume

39

pub_type

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