Drug Safety

, Volume 36, Supplement 1, pp 95–106 | Cite as

Empirical Performance of a Self-Controlled Cohort Method: Lessons for Developing a Risk Identification and Analysis System

  • Patrick B. Ryan
  • Martijn J. Schuemie
  • David Madigan
Original Research Article



Observational healthcare data offer the potential to enable identification of risks of medical products, but appropriate methodology has not yet been defined. The self-controlled cohort method, which compares the post-exposure outcome rate with the pre-exposure rate among an exposed cohort, has been proposed as a potential approach for risk identification but its performance has not been fully assessed.


To evaluate the performance of the self-controlled cohort method as a tool for risk identification in observational healthcare data.

Research Design

The method was applied to 399 drug-outcome scenarios (165 positive controls and 234 negative controls across 4 health outcomes of interest) in 5 real observational databases (4 administrative claims and 1 electronic health record) and in 6 simulated datasets with no effect and injected relative risks of 1.25, 1.5, 2, 4, and 10, respectively.


Method performance was evaluated through area under ROC curve (AUC), bias, and coverage probability.


The self-controlled cohort design achieved strong predictive accuracy across the outcomes and databases under study, with the top-performing settings exceeding AUC >0.76 in all scenarios. However, the estimates generated were observed to be highly biased with low coverage probability.


If the objective for a risk identification system is one of discrimination, the self-controlled cohort method shows promise as a potential tool for risk identification. However, if a system is intended to generate effect estimates to quantify the magnitude of potential risks, the self-controlled cohort method may not be suitable, and requires substantial calibration to be properly interpreted under nominal properties.


Sitagliptin Coverage Probability Incidence Rate Ratio Acute Liver Injury True Effect Size 
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 is funded by the Foundation for the National Institutes of Health through generous contributions from the following: Abbott, Amgen Inc., AstraZeneca, Bayer Healthcare Pharmaceuticals, Inc., Biogen Idec, Bristol-Myers Squibb, Eli Lilly & Company, GlaxoSmithKline, Janssen Research and Development, Lundbeck, Inc., Merck & Co., Inc., Novartis Pharmaceuticals Corporation, Pfizer Inc, Pharmaceutical Research Manufacturers of America (PhRMA), Roche, Sanofi-aventis, Schering-Plough Corporation, and Takeda. Drs. Ryan and Schuemie are employees of Janssen Research and Development. Dr. Schuemie received a fellowship from the Office of Medical Policy, Center for Drug Evaluation and Research, Food and Drug Administration. Drs. Schuemie and Madigan have 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

  • Patrick B. Ryan
    • 1
    • 4
  • Martijn J. Schuemie
    • 2
    • 4
  • David Madigan
    • 3
    • 4
  1. 1.Janssen Research and Development LLCTitusvilleUSA
  2. 2.Department of Medical InformaticsErasmus University Medical Center RotterdamRotterdamThe Netherlands
  3. 3.Department of StatisticsColumbia UniversityNew YorkUSA
  4. 4.Observational Medical Outcomes Partnership, Foundation for the National Institutes of HealthBethesdaUSA

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