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

TitleEfficient methods for signal detection from correlated adverse events in clinical trials.
Publication TypeJournal Article
Year of Publication2019
AuthorsDiao, Guoqing, Guanghan F. Liu, Donglin Zeng, William Wang, Xianming Tan, Joseph F. Heyse, and Joseph G. Ibrahim
JournalBiometrics
Volume75
Issue3
Pagination1000-1008
Date Published2019 09
ISSN1541-0420
KeywordsBias, Clinical Trials as Topic, Computer Simulation, Drug-Related Side Effects and Adverse Reactions, Humans, Monte Carlo Method, Patient Reported Outcome Measures
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 percent of the hypotheses are rejected under the null at the nominal significance level of . 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.

DOI10.1111/biom.13031
Alternate JournalBiometrics
Original PublicationEfficient methods for signal detection from correlated adverse events in clinical trials.
PubMed ID30690717
PubMed Central IDPMC6661211
Grant ListR01 GM070335 / GM / NIGMS NIH HHS / United States
R01 GM124104 / GM / NIGMS NIH HHS / United States