Event Summary:
A one-day workshop organized by the Center for Interdisciplinary Research and
Consulting at the University of Maryland, Baltimore County (UMBC), and the
Center for Innovative Study Design (CISD) at Stanford University in the field of Clinical Trials and Regulatory Science.
Registration is now closed
⇒View Archives here⇐
Organizing Committee:
Faculty from UMBC and Stanford University and leaders from FDA and industry.
For more information:
Please contact Dr. Yi Huang, Department of Mathematics and
Statistics, University of Maryland Baltimore County (UMBC),
1000 Hilltop Circle, Baltimore, MD 21250.
E-Mail: yihuang@umbc.edu.
For any Technical difficulties Please contact Zana Coulibaly at czana1@umbc.edu.
Participant Information
Ram Tiwari from Division of Biostatistics, .
Paper: Likelihood Ratio Test (LRT) based Methods for Signal Detection in FDA’s AERS Database
The statistical methods used for data-mining or signal detection of drug-adverse event combinations from large drug safety databases such as FDA’s Adverse Event Reporting System (FAERS), consisting of spontaneous reports on adverse events for post-market drugs are called passive surveillance methods. On the other hand, the statistical signal detection methods for longitudinal data, as the data accrues in time, are called active surveillance methods. A review of the most commonly used passive surveillance statistical methods, along with likelihood ratio test (LRT) based methods, will be discussed in detail.