Drug Safety

, Volume 36, Supplement 1, pp 181–193 | Cite as

Alternative Outcome Definitions and Their Effect on the Performance of Methods for Observational Outcome Studies

  • Christian G. ReichEmail author
  • Patrick B. Ryan
  • Martijn J. Schuemie
Original Research Article



A systematic risk identification system has the potential to test marketed drugs for important Health Outcomes of Interest or HOI. For each HOI, multiple definitions are used in the literature, and some of them are validated for certain databases. However, little is known about the effect of different definitions on the ability of methods to estimate their association with medical products.


Alternative definitions of HOI were studied for their effect on the performance of analytical methods in observational outcome studies.


A set of alternative definitions for three HOI were defined based on literature review and clinical diagnosis guidelines: acute kidney injury, acute liver injury and acute myocardial infarction. The definitions varied by the choice of diagnostic codes and the inclusion of procedure codes and lab values. They were then used to empirically study an array of analytical methods with various analytical choices in four observational healthcare databases. The methods were executed against predefined drug-HOI pairs to generate an effect estimate and standard error for each pair. These test cases included positive controls (active ingredients with evidence to suspect a positive association with the outcome) and negative controls (active ingredients with no evidence to expect an effect on the outcome). Three different performance metrics where used: (i) Area Under the Receiver Operator Characteristics (ROC) curve (AUC) as a measure of a method’s ability to distinguish between positive and negative test cases, (ii) Measure of bias by estimation of distribution of observed effect estimates for the negative test pairs where the true effect can be assumed to be one (no relative risk), and (iii) Minimal Detectable Relative Risk (MDRR) as a measure of whether there is sufficient power to generate effect estimates.


In the three outcomes studied, different definitions of outcomes show comparable ability to differentiate true from false control cases (AUC) and a similar bias estimation. However, broader definitions generating larger outcome cohorts allowed more drugs to be studied with sufficient statistical power.


Broader definitions are preferred since they allow studying drugs with lower prevalence than the more precise or narrow definitions while showing comparable performance characteristics in differentiation of signal vs. no signal as well as effect size estimation.


Acute Kidney Injury Diagnostic Code Alternative Definition Acute Liver Injury Observational Medical Outcome Partnership 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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, AstraZeneca, Bayer Healthcare Pharmaceuticals, Biogen Idec, Bristol-Myers Squibb, Eli Lilly & Company, GlaxoSmithKline, Janssen Research and Development, Lundbeck, Merck & Co., Novartis Pharmaceuticals Corporation, Pfizer, Pharmaceutical Research Manufacturers of America (PhRMA), Roche, Sanofi-Aventis, Schering-Plough Corporation, and Takeda. Dr. Reich is an employee of AstraZeneca. Drs. Ryan 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. Dr. Schuemie has previously received a grant 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 ( 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.


  1. 1.
    Barron BA. The effects of misclassification on the estimation of relative risk. Biometrics. 1977;33(2):414–8.PubMedCrossRefGoogle Scholar
  2. 2.
    Stang PE, Ryan PB, Racoosin JA, Overhage JM, Hartzema AG, Reich C, et al. Advancing the science for active surveillance: rationale and design for the Observational Medical Outcomes Partnership. Ann Int Med. 2010;153(9):600–6.PubMedCrossRefGoogle Scholar
  3. 3.
    Carnahan RM, Moores KG. Mini-Sentinel’s systematic reviews of validated methods for identifying health outcomes using administrative and claims data: methods and lessons learned. Pharmacoepidemiol Drug Saf. 2012;21(Suppl 1):82–9.PubMedCrossRefGoogle Scholar
  4. 4.
    Stang PE, Ryan PB, Dusetzina SB, Hartzema AG, Reich C, Overhage JM, et al. Health outcomes of interest in observational data: issues in identifying definitions in the literature. Health Outcomes Res Med. 2012;3(1):e37–44.CrossRefGoogle Scholar
  5. 5.
    Kellum JA, Bellomo R, Ronco C. Definition and classification of acute kidney injury. Nephron Clinical Pract. 2008;109(4):c182–7.CrossRefGoogle Scholar
  6. 6.
    James M, Pannu N. Methodological considerations for observational studies of acute kidney injury using existing data sources. J Nephrol. 2009;22(3):295–305.PubMedGoogle Scholar
  7. 7.
    Katz AJ, Ryan PB, Racoosin JA, Stang PE. Assessment of case definitions for identifying acute liver injury in large observational databases. Drug Saf. 2013;36(8):651–61.PubMedCrossRefGoogle Scholar
  8. 8.
    Ryan PB, Stang PE, Overhage JM, Suchard MA, Hartzema AG, DuMouchel W, et al. A comparison of the empirical performance of methods for a risk identification system. Drug Saf (in this supplement issue). doi: 10.1007/s40264-013-0108-9.
  9. 9.
    Observational Medical Outcomes Partnership Methods Library; 2012. [cited 2012 December 13].
  10. 10.
    Overhage JM, Ryan PB, Schuemie MJ, Stang PE. Desideratum for Evidence Based Epidemiology. Drug Saf (in this supplement issue). doi: 10.1007/s40264-013-0102-2
  11. 11.
    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
  12. 12.
    Armstrong B. A simple estimator of minimum detectable relative risk, sample size, or power in cohort studies. Am J Epidemiol. 1987;126(2):356–8.PubMedCrossRefGoogle Scholar
  13. 13.
    Reich C, Ryan PB, Suchard MA. The impact of drug and outcome prevalence on the feasibility and performance of analytical methods for a risk identification and analysis system. Drug Saf (in this supplement issue). doi: 10.1007/s40264-013-0112-0
  14. 14.
    Cantor SB, Kattan MW. Determining the area under the ROC curve for a binary diagnostic test. Med Decis Making. 2000;20(4):468–70.PubMedCrossRefGoogle Scholar
  15. 15.
    Copeland KT, Checkoway H, McMichael AJ, Holbrook RH. Bias due to misclassification in the estimation of relative risk. Am J Epidemiol. 1977;105(5):488–95.PubMedGoogle Scholar
  16. 16.
    Wacholder S, Armstrong B, Hartge P. Validation studies using an alloyed gold standard. Am J Epidemiol. 1993;137(11):1251–8.PubMedGoogle Scholar
  17. 17.
    Evans JM, MacDonald TM. Misclassification and selection bias in case-control studies using an automated database. Pharmacoepidemiol Drug Saf. 1997;6(5):313–8.PubMedCrossRefGoogle Scholar
  18. 18.
    Mukherjee D, Nissen SE, Topol EJ. Risk of cardiovascular events associated with selective COX-2 inhibitors. JAMA. 2001;286(8):954–9.PubMedCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Christian G. Reich
    • 1
    • 4
    Email author
  • Patrick B. Ryan
    • 2
    • 4
  • Martijn J. Schuemie
    • 3
    • 4
  1. 1.AstraZeneca PLCWalthamUSA
  2. 2.Janssen Research and Development LLCTitusvilleUSA
  3. 3.Department of Medical InformaticsErasmus University Medical Center RotterdamRotterdamThe Netherlands
  4. 4.Observational Medical Outcomes PartnershipFoundation for the National Institutes of HealthBethesdaUSA

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