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A Comparison of the Empirical Performance of Methods for a Risk Identification System

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Abstract

Background

Observational healthcare data offer the potential to enable identification of risks of medical products, and the medical literature is replete with analyses that aim to accomplish this objective. A number of established analytic methods dominate the literature but their operating characteristics in real-world settings remain unknown.

Objectives

To compare the performance of seven methods (new user cohort, case control, self-controlled case series, self-controlled cohort, disproportionality analysis, temporal pattern discovery, and longitudinal gamma poisson shrinker) as tools for risk identification in observational healthcare data.

Research Design

The experiment applied each method 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).

Measures

Method performance was evaluated through Area Under the receiver operator characteristics Curve (AUC), bias, mean square error, and confidence interval coverage probability.

Results

Multiple methods offer strong predictive accuracy, with AUC > 0.70 achievable for all outcomes and databases with more than one analytical approach. Self-controlled methods (self-controlled case series, temporal pattern discovery, self-controlled cohort) had higher predictive accuracy than cohort and case–control methods across all databases and outcomes. Methods differed in the expected value and variance of the error distribution. All methods had lower coverage probability than the expected nominal properties.

Conclusions

Observational healthcare data can inform risk identification of medical product effects on acute liver injury, acute myocardial infarction, acute renal failure and gastrointestinal bleeding. However, effect estimates from all methods require calibration to address inconsistency in method operating characteristics. Further empirical evaluation is required to gauge the generalizability of these findings to other databases and outcomes.

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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. Drs. Ryan, Stang 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, Suchard, Madigan, and Hartzema have received a grant previously from FNIH. Dr. DuMouchel is an employee of Oracle Health Sciences. Dr. Overhage is an employee of Siemens. Christian Reich is an employee of Astra-Zeneca.

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.

Author information

Authors and Affiliations

  1. Janssen Research and Development LLC, 1125 Trenton-Harbourton Road, Room K30205, PO Box 200, Titusville, NJ, 08560, USA

    Patrick B. Ryan & Paul E. Stang

  2. Siemens Health Services, Malvern, PA, USA

    J. Marc Overhage

  3. Departments of Biomathematics and Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA, USA

    Marc A. Suchard

  4. Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles, CA, USA

    Marc A. Suchard

  5. College of Pharmacy, University of Florida, Gainesville, FL, USA

    Abraham G. Hartzema

  6. Oracle Health Sciences, Burlington, MA, USA

    William DuMouchel

  7. AstraZeneca, Waltham, MA, USA

    Christian G. Reich

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

    Martijn J. Schuemie

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

    David Madigan

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

    Patrick B. Ryan, Paul E. Stang, J. Marc Overhage, Marc A. Suchard, Abraham G. Hartzema, William DuMouchel, Christian G. Reich, Martijn J. Schuemie & David Madigan

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  1. Patrick B. Ryan
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  8. Martijn J. Schuemie
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  9. David Madigan
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Corresponding author

Correspondence to Patrick B. Ryan.

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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Ryan, P.B., Stang, P.E., Overhage, J.M. et al. A Comparison of the Empirical Performance of Methods for a Risk Identification System. Drug Saf 36 (Suppl 1), 143–158 (2013). https://doi.org/10.1007/s40264-013-0108-9

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  • DOI: https://doi.org/10.1007/s40264-013-0108-9

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