Statistics and Data Science Seminar
Prof. Dulal Bhaumik
UIC
Random-effect Poisson Regression Analysis of Adverse Event Reports: The Relationship Between Antidepressants and Suicide
Abstract: A new statistical methodology is developed for analysis of spontaneous adverse event reports from post-marketing
drug surveillance data. The method involves both empirical Bayes and fully-Bayes estimation of rate multipliers
for each drug within a class of drugs, for a particular adverse event, based on a mixed-effects Poisson regression model.
Both parametric and semi-parametric models for the random effect distribution are examined. The method is applied to
data from FDA`s Adverse Event Reporting System (AERS) on the relationship between antidepressants and suicide.
We obtain point estimates and 95% confidence intervals for the rate multiplier for each drug (e.g., antidepressants),
which can be used to determine if a particular drug has an increased risk of association with a particular adverse event
(e.g., suicide). Confidence intervals that do not include 1.0 provide evidence for either significant protective or
harmful associations of the drug and the adverse effect. We also examine empirical Bayes, parametric Bayes and
semi-parametric Bayes estimators of the rate multipliers and associated confidence intervals. Results of our analysis
of the FDA AERS data revealed that newer antidepressants are associated with lower rates of suicide. This finding
contradicts previous findings of FDA that newer antidepressants are causally related to increased suicidal thinking
in children and young adults. Finally, we suggest changes in the AERS system to improve our ability to discover these
adverse events.
Wednesday November 18, 2009 at 3:00 PM in SEO 636