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

Abstract

Background

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.

Objectives

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.

Measures

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.

Results

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.

Conclusions

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.

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Acknowledgements

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 (http://www.imi.europa.eu) 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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Correspondence to Marc A. Suchard.

Additional information

This work was partially supported by National Science Foundation grant IIS 1251151. The OMOP research used data from Truven Health Analytics (formerly the Health Business of Thomson Reuters), and includes MarketScan® Research Databases, represented with MarketScan Lab Supplemental (MSLR, 1.2 m persons), MarketScan Medicare Supplemental Beneficiaries (MDCR, 4.6 m persons), MarketScan Multi-State Medicaid (MDCD, 10.8 m persons), MarketScan Commercial Claims and Encounters (CCAE, 46.5 m persons). Data also provided by Quintiles® Practice Research Database (formerly General Electric’s Electronic Health Record, 11.2 m persons) database. GE is an electronic health record database while the other four databases contain administrative claims data.

Appendix

Appendix

figure a

Self-controlled case series design estimates for all test cases stratified by data source

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Suchard, M.A., Zorych, I., Simpson, S.E. et al. Empirical Performance of the Self-Controlled Case Series Design: Lessons for Developing a Risk Identification and Analysis System. Drug Saf 36, 83–93 (2013). https://doi.org/10.1007/s40264-013-0100-4

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Keywords

  • Salmeterol
  • Coverage Probability
  • Design Choice
  • Acute Liver Injury
  • Risk Identification