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UMC investigates use of observational data to id ADRs

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References

  1. Norén GN, et al. Empirical Performance of the Calibrated Self-Controlled Cohort Analysis Within Temporal Pattern Discovery: Lessons for Developing a Risk Identification and Analysis System. Drug Safety 36 (Suppl.): 107-121, No. 1, Oct 2013. Available from: URL: http://dx.doi.org/10.1007/s40264-013-0095-x

  2. Ryan PB, et al. A Comparison of the Empirical Performance of Methods for a Risk Identification System. Drug Safety 36 (Suppl.): 143-158, No. 1, Oct 2013. Available from: URL: http://dx.doi.org/10.1007/s40264-013-0108-9

  3. Schuemie MJ, et al. Replication of the OMOP Experiment in Europe: Evaluating Methods for Risk Identification in Electronic Health Record Databases. Drug Safety 36 (Suppl.): 159-169, No. 1, Oct 2013. Available from: URL: http://dx.doi.org/10.1007/s40264-013-0109-8

  4. The Uppsala Monitoring Centre. Analysis of patient records and insurance claims can help identify side-effects of drugs. Media Release : 5 Nov 2013. Available from: URL: http://www.who-umc.org

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UMC investigates use of observational data to id ADRs. Reactions Weekly 1481, 2 (2013). https://doi.org/10.1007/s40278-013-7552-2

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