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Volume 7, Issue 4 (Suppl)
J Clin Trial
ISSN: 2167-0870 JCTR, an open access journal
Global Pharmacovigilance 2017
July 06-07, 2017
JULY 06-07, 2017 KUALA LUMPUR, MALAYSIA
8
TH
GLOBAL
Pharmacovigilance &
Drug Safety Summit
Comparison of signal detection methods in pharmacovigilance
Sanjeev Miglani
APCER Life Sciences, India
T
he pharmaceutical companies collect the adverse events (AE) data from varied sources, and this collected data need to be
analyzed for the safety surveillance. Spontaneous reporting (SR) adverse event system databases, large clinical projects and
health records databases contain data that may be valuable for timely detection of potential risks associated with drugs, devices,
and vaccines. All of the data sources include many different AEs and many medical products, so that any approach designed to
identify critical signals of potential harm must have adequate specificity to protect against false alarms yet provide acceptable
sensitivity for detecting issues that really need further investigation. The algorithms may seek to identify potential drug-event
associations without any prior specifications, to identify events associated with a particular product or set of products, or to
identify products associated with a particular event or set of events. A whole range of statistical methods have been applied for
data mining and signal detection in pharmacovigilance. Primarily there are frequentist as well as Bayesian approaches to SD.
This session will provide guidance to various approaches for signal detection. This session will provide recommendations for
using data from post marketing spontaneous adverse event reporting databases to provide insight into safety signals and offer
guidance regarding appropriate methods like frequentist and Bayesian approaches to use in various situations.
sanjeev.miglani@apcerls.comJ Clin Trial 2017, 7:4 (Suppl)
DOI: 10.4172/2167-0870-C1-017