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SUMMARY:Extracting Adverse Drug Events from Spontaneous Reporting Data for
  Understanding Drug Side Effects - Ines Smit\, University of Cambridge
DTSTART:20180307T141500Z
DTEND:20180307T143500Z
UID:TALK96268@talks.cam.ac.uk
CONTACT:Lisa Masters
DESCRIPTION:Adverse drug reactions remain difficult to accurately predict 
 during drug development. Some adverse events are not detected before a dru
 g is widely marketed\, causing harm to patients. The effects of drug combi
 nations are also difficult to predict\, potentially resulting in adverse d
 rug reactions due to drug-drug interactions. Therefore\, there is a need f
 or computational methods to aid the prediction of adverse events. In contr
 ast to pre-clinical and clinical data\, post-marketing surveillance of adv
 erse events is based on large numbers of reports from a diverse patient po
 pulation\, for example in terms of concomitant medications. While this mak
 es spontaneous reporting data particularly interesting to the study of adv
 erse events\, the data also suffers from a range of biases such as potenti
 al confounding factors\, complicating the discovery of drug-adverse event 
 associations. The application of propensity score matching\, a statistical
  technique that addresses potential confounders by selecting subsets of pa
 tients with similar baseline characteristics for comparison\, previously s
 howed promise for analysing spontaneous reporting data. Here\, we describe
  the implementation of a propensity score model on the largest publicly av
 ailable standardised version of the Food and Drug Administration Adverse E
 vent Reporting System to date. The results show that the approach reduces 
 bias related to drug prescription between patient groups\, improving condi
 tions for causal inference about adverse drug reactions. Currently\, adver
 se events associated with individual drugs have been calculated using the 
 method\, and the adverse events associated with risperidone and cerivastat
 in are presented as examples. Future work will include extending the metho
 d to detect drug-drug interactions. Another important part of future work 
 is the integration of drug-adverse event associations with other chemical 
 and biological data to help explain and predict adverse drug effects and d
 rug interactions.
LOCATION:Department of Chemistry\, Cambridge\, Unilever lecture theatre
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