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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

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Abstract

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.

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Acknowledgments

The Observational Medical Outcomes Partnership is funded by the Foundation for the National Institutes of Health (FNIH) 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. Dr. Ryan is an employee of Janssen Research and Development, and a past employee of GlaxoSmithKline, but does not receive compensation for his work with OMOP. Dr. Schuemie has become an employee of Janssen Research and Development since completing the work described here. 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 receive funding from FNIH. Dr. Norén, Mr. Bergvall and Ms. Juhlin were partially funded by FNIH grant NOREN11OMOP. The authors appreciate the thoughtful feedback from I. Ralph Edwards at the Uppsala Monitoring Centre, the OMOP Research Investigators, Executive Board, and Advisory Boards. No compensation was given for the reviews.

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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Authors and Affiliations

  1. Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden

    G. Niklas Norén, Tomas Bergvall & Kristina Juhlin

  2. Department of Mathematics, Stockholm University, Stockholm, Sweden

    G. Niklas Norén

  3. Janssen Research and Development LLC, Titusville, NJ, USA

    Patrick B. Ryan

  4. Department of Medical Informatics, Erasmus University Medical Center Rotterdam, Rotterdam, The Netherlands

    Martijn J. Schuemie

  5. Department of Statistics, Columbia University, New York, NY, USA

    David Madigan

  6. Observational Medical Outcomes Partnership, Foundation for the National Institutes of Health, Bethesda, MD, USA

    Patrick B. Ryan, Martijn J. Schuemie & David Madigan

Authors
  1. G. Niklas Norén
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  2. Tomas Bergvall
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  3. Patrick B. Ryan
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  4. Kristina Juhlin
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  5. Martijn J. Schuemie
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  6. David Madigan
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Corresponding author

Correspondence to G. Niklas Norén.

Additional information

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.

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Norén, G.N., Bergvall, T., Ryan, P.B. 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 Saf 36 (Suppl 1), 107–121 (2013). https://doi.org/10.1007/s40264-013-0095-x

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