A Comparison of the Empirical Performance of Methods for a Risk Identification System
- 645 Downloads
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.
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.
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).
Method performance was evaluated through Area Under the receiver operator characteristics Curve (AUC), bias, mean square error, and confidence interval coverage probability.
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.
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.
KeywordsCoverage Probability Acute Liver Injury Analysis Choice Observational Medical Outcome Partnership Confidence Interval Coverage
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.
- 5.Food and Drug Administration Amendments Act of 2007. p. Public Law 110-85, 21 STAT. 823 (2007).Google Scholar
- 6.FDA. The Sentinel Initiative: A National Strategy for Monitoring Medical Product Safety. May 2008 [cited 2012 September 15]. http://www.fda.gov/Safety/FDAsSentinelInitiative/ucm089474.htm.
- 7.FDA Drug Safety Communication: Update on the risk for serious bleeding events with the anticoagulant Pradaxa (dabigatran). November 2, 2012 [cited 2012 December 1]. http://www.fda.gov/Drugs/DrugSafety/ucm326580.htm.
- 8.DuMouchel B, Ryan PB, Schuemie MJ, Madigan D. Evaluation of disproportionality safety signaling applied to health care databases. Drug Saf (in this supplement issue).doi: 10.1007/s40264-013-0106-y.
- 9.Madigan D, Schuemie MJ, Ryan PB. Empirical performance of the case–control method: lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi: 10.1007/s40264-013-0105-z.
- 10.Norén GN, Bergvall T, Ryan PB, Juhlin K, Schuemie MJ, Madigna D. Empirical performance of the calibrated self-controlled cohort analysis within Temporal Pattern Discovery: lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi: 10.1007/s40264-013-0095-x.
- 11.Ryan PB, Schuemie MJ, Gruber S, Zorych I, Madigan D. Empirical performance of a new user cohort method: lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi: 10.1007/s40264-013-0099-6.
- 12.Ryan PB, Schuemie MJ, Madigan D. Empirical performance of a self-controlled cohort method: lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi: 10.1007/s40264-013-0101-3.
- 13.Schuemie MJ, Madigan D, Ryan PB. Empirical performance of Longitudinal Gamma Poisson Shrinker (LGPS) and Longitudinal Evaluation of Observational Profiles of Adverse events Related to Drugs (LEOPARD): lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue). doi: 10.1007/s40264-013-0107-x.
- 14.Suchard MA, Zorych I, Simpson SE, Schuemie MJ, Ryan PB, Madigan D. Empirical performance of the self-controlled case series design: lessons for developing a risk identification and analysis system. Drug Saf (in this supplement issue).doi: 10.1007/s40264-013-0100-4.
- 16.Ryan PB, Schuemie MJ, Welebob E, Duke J, Valentine S, Hartzema AG. Defining a reference set to support methodological research in drug safety. Drug Saf (in this supplement issue). doi: 10.1007/s40264-013-0097-8.
- 21.Bombardier C, Laine L, Reicin A, Shapiro D, Burgos-Vargas R, Davis B, et al. Comparison of upper gastrointestinal toxicity of rofecoxib and naproxen in patients with rheumatoid arthritis. VIGOR Study Group. N Engl J Med. 2000;343(21):1520–8 (2 p following 8).Google Scholar
- 25.Graham DJ, Campen D, Hui R, Spence M, Cheetham C, Levy G, et al. Risk of acute myocardial infarction and sudden cardiac death in patients treated with cyclo-oxygenase 2 selective and non-selective non-steroidal anti-inflammatory drugs: nested case–control study. Lancet. 2005;365(9458):475–81.PubMedGoogle Scholar
- 31.Schuemie MJ, Coloma PM, Straatman H, Herings RM, Trifirò G, Matthews JN, et al. Using electronic health care records for drug safety signal detection: a comparative evaluation of statistical methods. Med Care. 2012.Google Scholar
- 37.Tata LJ, Fortun PJ, Hubbard RB, Smeeth L, Hawkey CJ, Smith CJ, et al. Does concurrent prescription of selective serotonin reuptake inhibitors and non-steroidal anti-inflammatory drugs substantially increase the risk of upper gastrointestinal bleeding? Aliment Pharmacol Ther. 2005;22(3):175–81.PubMedCrossRefGoogle Scholar
- 41.Tisdale J, Miller D. Drug-induced diseases: prevention, detection, and management. 2nd ed. American Society of Health-System Pharmacists; 2010.Google Scholar