Drug Safety

, Volume 36, Supplement 1, pp 83–93 | Cite as

Empirical Performance of the Self-Controlled Case Series Design: Lessons for Developing a Risk Identification and Analysis System

  • Marc A. Suchard
  • Ivan Zorych
  • Shawn E. Simpson
  • Martijn J. Schuemie
  • Patrick B. Ryan
  • David Madigan
Original Research Article



The self-controlled case series (SCCS) offers potential as an statistical method for risk identification involving medical products from large-scale observational healthcare data. However, analytic design choices remain in encoding the longitudinal health records into the SCCS framework and its risk identification performance across real-world databases is unknown.


To evaluate the performance of SCCS and its design choices as a tool for risk identification in observational healthcare data.

Research Design

We examined the risk identification performance of SCCS across five design choices using 399 drug-health outcome pairs in five real observational databases (four administrative claims and one electronic health records). In these databases, the pairs involve 165 positive controls and 234 negative controls. We also consider several synthetic databases with known relative risks between drug-outcome pairs.


We evaluate risk identification performance through estimating the area under the receiver-operator characteristics curve (AUC) and bias and coverage probability in the synthetic examples.


The SCCS achieves strong predictive performance. Twelve of the twenty health outcome-database scenarios return AUCs >0.75 across all drugs. Including all adverse events instead of just the first per patient and applying a multivariate adjustment for concomitant drug use are the most important design choices. However, the SCCS as applied here returns relative risk point-estimates biased towards the null value of 1 with low coverage probability.


The SCCS recently extended to apply a multivariate adjustment for concomitant drug use offers promise as a statistical tool for risk identification in large-scale observational healthcare databases. Poor estimator calibration dampens enthusiasm, but on-going work should correct this short-coming.


Salmeterol Coverage Probability Design Choice Acute Liver Injury Risk Identification 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The Observational Medical Outcomes Partnership was funded by the Foundation for the National Institutes of Health through generous contributions from the following: Abbott, Amgen Inc., AstraZeneca, Bayer Healthcare Pharmaceuticals, Inc., Bristol - Myers Squibb, Eli Lilly & Company, GlaxoSmithKline, Johnson & Johnson, Lundbeck, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Pfizer Inc, Pharmaceutical Research Manufacturers of America (PhRMA), Roche, Sanofi-aventis, Schering-Plough Corporation, Takeda and Biogen Idec. Dr. Ryan is a past employee of GlaxoSmithKline, but does not receive compensation for his work with OMOP. Dr. Schuemie received a fellowship from the Office of Medical Policy, Center for Drug Evaluation and Research, Food and Drug Administration and is presently an employee of Janssen Research and Development. Drs. Suchard and Madigan received funding from FNIH.

This article was published in a supplement sponsored by the Foundation for the National Institutes of Health (FNIH). The supplement was guest edited by Stephen J.W. Evans. It was peer reviewed by Olaf H. Klungel who received a small honorarium to cover out-of-pocket expenses. S.J.W.E has received travel funding from the FNIH to travel to the OMOP symposium and received a fee from FNIH for the review of a protocol for OMOP. O.H.K has received funding for the IMI-PROTECT project from the Innovative Medicines Initiative Joint Undertaking ( under Grant Agreement no 115004, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution.


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Marc A. Suchard
    • 1
    • 2
    • 3
  • Ivan Zorych
    • 4
  • Shawn E. Simpson
    • 4
  • Martijn J. Schuemie
    • 5
  • Patrick B. Ryan
    • 6
  • David Madigan
    • 4
  1. 1.Department of Biomathematics, David Geffen School of Medicine at UCLAUniversity of CaliforniaLos AngelesUSA
  2. 2.Department of Human Genetics, David Geffen School of Medicine at UCLAUniversity of CaliforniaLos AngelesUSA
  3. 3.Department of Biostatistics, UCLA Fielding School of Public HealthUniversity of CaliforniaLos AngelesUSA
  4. 4.Department of StatisticsColumbia UniversityNew YorkUSA
  5. 5.Department of Medical InformationsErasmus University Medical CenterRotterdamThe Netherlands
  6. 6.Janssen Research and DevelopmentTitusvilleUSA

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