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01-10-2013 | Original Research Article

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

Authors: 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

Published in: Drug Safety | Special Issue 1/2013

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.

Atkins D, Best D, Briss PA, Eccles M, Falck-Ytter Y, Flottorp S, et al. Grading quality of evidence and strength of recommendations. BMJ. 2004;328(7454):1490.PubMedCrossRef

Berlin JA, Glasser SC, Ellenberg SS. Adverse event detection in drug development: recommendations and obligations beyond phase 3. Am J Public Health. 2008;98(8):1366–71.PubMedCrossRef

Waller PC, Evans SJ. A model for the future conduct of pharmacovigilance. Pharmacoepidemiol Drug Saf. 2003;12(1):17–29.PubMedCrossRef

Ioannidis JP. Why most published research findings are false. PLoS Med. 2005;2(8):e124.PubMedCrossRef

Food and Drug Administration Amendments Act of 2007. p. Public Law 110-85, 21 STAT. 823 (2007).

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.

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.

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.

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.

15.

Schuemie MJ, Ryan PB, DuMouchel W, Suchard MA, Madigan D. Interpreting observational studies: why empirical calibration is needed to correct p-values. Stat Med. 2013. doi:10.1002/sim.5925.PubMed

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.

17.

Greenland S. Confounding and exposure trends in case-crossover and case-time-control designs. Epidemiology. 1996;7(3):231–9.PubMedCrossRef

18.

Maclure M. The case-crossover design: a method for studying transient effects on the risk of acute events. Am J Epidemiol. 1991;133(2):144–53.PubMed

19.

Maclure M, Fireman B, Nelson JC, Hua W, Shoaibi A, Paredes A, et al. When should case-only designs be used for safety monitoring of medical products? Pharmacoepidemiol Drug Saf. 2012;21 Suppl 1:50–61.PubMedCrossRef

20.

Gagne JJ, Fireman B, Ryan PB, Maclure M, Gerhard T, Toh S, et al. Design considerations in an active medical product safety monitoring system. Pharmacoepidemiol Drug Saf. 2012;21(Suppl 1):32–40.PubMedCrossRef

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).

22.

Bresalier RS, Sandler RS, Quan H, Bolognese JA, Oxenius B, Horgan K, et al. Cardiovascular events associated with rofecoxib in a colorectal adenoma chemoprevention trial. N Engl J Med. 2005;352(11):1092–102.PubMedCrossRef

23.

Nissen SE, Wolski K. Effect of rosiglitazone on the risk of myocardial infarction and death from cardiovascular causes. N Engl J Med. 2007;356(24):2457–71.PubMedCrossRef

24.

Cunnington M, Webb D, Qizilbash N, Blum D, Mander A, Funk MJ, et al. Risk of ischaemic cardiovascular events from selective cyclooxygenase-2 inhibitors in osteoarthritis. Pharmacoepidemiol Drug Saf. 2008;17(6):601–8.PubMedCrossRef

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.PubMed

26.

Brown JS, Kulldorff M, Chan KA, Davis RL, Graham D, Pettus PT, et al. Early detection of adverse drug events within population-based health networks: application of sequential testing methods. Pharmacoepidemiol Drug Saf. 2007;16(12):1275–84.PubMedCrossRef

27.

Brownstein JS, Sordo M, Kohane IS, Mandl KD. The tell-tale heart: population-based surveillance reveals an association of rofecoxib and celecoxib with myocardial infarction. PLoS ONE. 2007;2(9):e840.PubMedCrossRef

28.

Brownstein JS, Murphy SN, Goldfine AB, Grant RW, Sordo M, Gainer V, et al. Rapid identification of myocardial infarction risk associated with diabetes medications using electronic medical records. Diabetes Care. 2010;33(3):526–31.PubMedCrossRef

29.

Graham DJ, Ouellet-Hellstrom R, MaCurdy TE, Ali F, Sholley C, Worrall C, et al. Risk of acute myocardial infarction, stroke, heart failure, and death in elderly Medicare patients treated with rosiglitazone or pioglitazone. JAMA. 2010;304(4):411–8.PubMedCrossRef

30.

Ryan PB, Madigan D, Stang PE, Marc Overhage J, Racoosin JA, Hartzema AG. Empirical assessment of methods for risk identification in healthcare data: results from the experiments of the Observational Medical Outcomes Partnership. Stat Med. 2012;31(30):4401–15.PubMedCrossRef

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.

32.

Coloma PM, Avillach P, Salvo F, Schuemie MJ, Ferrajolo C, Pariente A, et al. A reference standard for evaluation of methods for drug safety signal detection using electronic healthcare record databases. Drug Saf. 2013;36(1):13–23.PubMedCrossRef

33.

Glanz JM, McClure DL, Xu S, Hambidge SJ, Lee M, Kolczak MS, et al. Four different study designs to evaluate vaccine safety were equally validated with contrasting limitations. J Clin Epidemiol. 2006;59(8):808–18.PubMedCrossRef

34.

Nicholas JM, Grieve AP, Gulliford MC. Within-person study designs had lower precision and greater susceptibility to bias because of trends in exposure than cohort and nested case–control designs. J Clin Epidemiol. 2012;65(4):384–93.PubMedCrossRef

35.

Ramsay EN, Pratt NL, Ryan P, Roughead EE. Proton pump inhibitors and the risk of pneumonia: a comparison of cohort and self-controlled case series designs. BMC Med Res Methodol. 2013;13(1):82.PubMedCrossRef

36.

Hubbard R, Farrington P, Smith C, Smeeth L, Tattersfield A. Exposure to tricyclic and selective serotonin reuptake inhibitor antidepressants and the risk of hip fracture. Am J Epidemiol. 2003;158(1):77–84.PubMedCrossRef

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.PubMedCrossRef

38.

Schneeweiss S. A basic study design for expedited safety signal evaluation based on electronic healthcare data. Pharmacoepidemiol Drug Saf. 2010;19(8):858–68.PubMedCrossRef

39.

Pearce N. Classification of epidemiological study designs. Int J Epidemiol. 2012;41(2):393–7.PubMedCrossRef

40.

Trifirò G, Pariente A, Coloma PM, Kors JA, Polimeni G, Miremont-Salame G, et al. Data mining on electronic health record databases for signal detection in pharmacovigilance: which events to monitor? Pharmacoepidemiol Drug Saf. 2009;18(12):1176–84.PubMedCrossRef

41.

Tisdale J, Miller D. Drug-induced diseases: prevention, detection, and management. 2nd ed. American Society of Health-System Pharmacists; 2010.

Title: A Comparison of the Empirical Performance of Methods for a Risk Identification System
Authors: 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
Publication date: 01-10-2013
Publisher: Springer International Publishing
Published in: Drug Safety / Issue Special Issue 1/2013
Print ISSN: 0114-5916
Electronic ISSN: 1179-1942
DOI: https://doi.org/10.1007/s40264-013-0108-9

Keynote webinar | Spotlight on medication adherence

Springer Medicine

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

Abstract

Background

Objectives

Research Design

Measures

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Objectives

Research Design

Measures

Results

Conclusions

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Empirical Performance of the Case–Control Method: Lessons for Developing a Risk Identification and Analysis System

Replication of the OMOP Experiment in Europe: Evaluating Methods for Risk Identification in Electronic Health Record Databases

Studying the Science of Observational Research: Empirical Findings from the Observational Medical Outcomes Partnership

Defining a Reference Set to Support Methodological Research in Drug Safety

Managing Data Quality for a Drug Safety Surveillance System

Empirical Performance of the Calibrated Self-Controlled Cohort Analysis Within Temporal Pattern Discovery: Lessons for Developing a Risk Identification and Analysis System