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Activity Number: 78 - Bayesian Generalized Linear Models for Medicine
Type: Contributed
Date/Time: Sunday, July 29, 2018 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #328877
Author(s): Yu Gao* and Kun Liang
Companies: University of Waterloo and University of Waterloo
Keywords: signal detection; false discovery rate; pharmacovigilance data; local FDR; true positive

The World Health Organization and many countries have built pharmacovigilance databases to detect potential adverse effects due to marketed drugs. Although a number of methods have been devel- oped for early detection of adverse drug effects, the vast majority of them do not consider the multiplicity arising from testing thousands of drug and adverse event combinations. We first derive the optimal statistic to maximize the power of detection while maintaining proper error rate. We then propose a nonparametric empirical Bayes method to estimate the optimal statistic and demonstrate its superior perfor- mance through simulation. Finally, the proposed method is applied to the pharmacovigilance database in the United Kingdom.

Authors who are presenting talks have a * after their name.

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