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Empirical Performance of the Calibrated Self-Controlled Cohort Analysis Within Temporal Pattern Discovery: Lessons for Developing a Risk Identification and Analysis System
Stockholm University, Faculty of Science, Department of Mathematics.
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2013 (English)In: Drug Safety, ISSN 0114-5916, Vol. 36, s107-S121 p.Article in journal (Refereed) Published
Abstract [en]

Background Observational healthcare data offer the potential to identify adverse drug reactions that may be missed by spontaneous reporting. The self-controlled cohort analysis within the Temporal Pattern Discovery framework compares the observed-to-expected ratio of medical outcomes during post-exposure surveillance periods with those during a set of distinct pre-exposure control periods in the same patients. It utilizes an external control group to account for systematic differences between the different time periods, thus combining within- and between-patient confounder adjustment in a single measure. Objectives To evaluate the performance of the calibrated self-controlled cohort analysis within Temporal Pattern Discovery as a tool for risk identification in observational healthcare data. Research Design Different implementations of the calibrated self-controlled cohort analysis were applied to 399 drug-outcome pairs (165 positive and 234 negative test cases across 4 health outcomes of interest) in 5 real observational databases (four with administrative claims and one with electronic health records). Measures Performance was evaluated on real data through sensitivity/specificity, the area under receiver operator characteristics curve (AUC), and bias. Results The calibrated self-controlled cohort analysis achieved good predictive accuracy across the outcomes and databases under study. The optimal design based on this reference set uses a 360 days surveillance period and a single control period 180 days prior to new prescriptions. It achieved an average AUC of 0.75 and AUC >0.70 in all but one scenario. A design with three separate control periods performed better for the electronic health records database and for acute renal failure across all data sets. The estimates for negative test cases were generally unbiased, but a minor negative bias of up to 0.2 on the RR-scale was observed with the configurations using multiple control periods, for acute liver injury and upper gastrointestinal bleeding. Conclusions The calibrated self-controlled cohort analysis within Temporal Pattern Discovery shows promise as a tool for risk identification; it performs well at discriminating positive from negative test cases. The optimal parameter configuration may vary with the data set and medical outcome of interest.

Place, publisher, year, edition, pages
2013. Vol. 36, s107-S121 p.
National Category
Pharmacology and Toxicology
URN: urn:nbn:se:su:diva-97403DOI: 10.1007/s40264-013-0095-xISI: 000326765900011OAI: diva2:678027


Funding agencies:

Foundation for the National Institutes of Health (FNIH) Office of Medical Policy, Center for Drug Evaluation and Research, Food and Drug Administration; FNIH, NOREN11OMOP;Innovative Medicines Initiative Joint Undertaking 115004; European Union's Seventh Framework Programme 

Available from: 2013-12-11 Created: 2013-12-09 Last updated: 2013-12-11Bibliographically approved

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Norén, G. Niklas
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