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Activity Number: 15
Type: Topic Contributed
Date/Time: Sunday, August 9, 2015 : 2:00 PM to 3:50 PM
Sponsor: Biopharmaceutical Section
Abstract #316967
Title: Which Needles Are Not in the Haystack? Linking Evidence to Support the Establishment of a Reference Standard of Negative Controls for Pharmacovigilance
Author(s): Richard D. Boyce* and Erica Voss and Christian Reich and Nicholas Tatonetti and Patrick Ryan
Companies: University of Pittsburgh and Janssen R&D and AstraZeneca and Columbia University and Observational Health Data Sciences and Informatics
Keywords: pharmacovigilance ; p-value calibration ; negative controls ; evidence base
Abstract:

False positive associations between drugs and health outcome of interest (HOIs) are a common problem in pharmacovigilance. One promising approach to reducing the occurrence of false positives is the calibration of p-values using drug-outcome pairs for which it is likely that no causal relationship exists. Previous studies using the approach have focused on only a few HOIs. In this paper, we describe an evidence base that will enable the method to be scaled to all possible drug-HOI pairs. Evidence about the potential relationship between drugs and HOIs from multiple disparate sources were integrated into a common schema and set of terminologies. Methods were developed to query for drug-HOI evidence and metadata across all sources.

The new evidence base integrates drug-HOI evidence from spontaneous reports (counts and statistical signals), scientific literature (PubMed and SemMedDB), American and European product labeling, and clinical trials (ClinicalTrials.gov). Machine learning methods that can predict negative drug-HOI associations are being built using features in the evidence base, and then tested for their performance characteristics.


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

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