Event Charts for the Analysis of Adverse Events in Longitudinal Studies: An Example from a Smoking Cessation Pharmacotherapy Trial
Joel A. Dubin*, 1, Stephanie S. O'Malley2
Identifiers and Pagination:Year: 2010
First Page: 34
Last Page: 41
Publisher Id: TOEPIJ-3-34
Article History:Received Date: 04/03/2010
Revision Received Date: 01/06/2010
Acceptance Date: 07/06/2010
Electronic publication date: 8/7/2010
Collection year: 2010
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
To illustrate the use of one particular graphical method, the event chart (Lee et al., 2000), for the display of adverse events (AE’s), along with other important considerations such as time on treatment/intervention, severity of AE’s, treatment assignment, gender, etc., in longitudinal studies. These graphs can also include other key information such as efficacy measures and time-dependent covariates of interest.
Emphasizing an application of a dose-ranging smoking cessation trial of naltrexone, we use event charts to convey a few potentially interesting findings from the complex data from this trial, with particular attention paid to the analysis of the safety (AE) data from the subset of individuals who dropped out before the end of the treatment phase of the study
The event charts conveyed some interesting findings regarding relationships between gender, AE’s and dropout time, as well as between treatment group, AE’s and dropout time, and between AE burden and dropout time.
Event charts can be one of the helpful exploratory tools in investigating the pattern of adverse events and their possible association with covariates and time on treatment/intervention in longitudinal studies. Findings from the event chart analysis of AE’s could potentially lead to more formal statistical analysis and modeling. Software for generating these event charts is available in R and S-Plus.