Skip to main content
Log in

Variation in Choice of Study Design: Findings from the Epidemiology Design Decision Inventory and Evaluation (EDDIE) Survey

  • Original Research Article
  • Published:
Drug Safety Aims and scope Submit manuscript



Researchers using observational data to understand drug effects must make a number of analytic design choices that suit the characteristics of the data and the subject of the study. Review of the published literature suggests that there is a lack of consistency even when addressing the same research question in the same database.


To characterize the degree of similarity or difference in the method and analysis choices made by observational database research experts when presented with research study scenarios.

Research Design

On-line survey using research scenarios on drug-effect studies to capture method selection and analysis choices that follow a dependency branching based on response to key questions.


Voluntary participants experienced in epidemiological study design solicited for participation through registration on the Observational Medical Outcomes Partnership website, membership in particular professional organizations, or links in relevant newsletters.


Description (proportion) of respondents selecting particular methods and making specific analysis choices based on individual drug-outcome scenario pairs. The number of questions/decisions differed based on stem questions of study design, time-at-risk, outcome definition, and comparator.


There is little consistency across scenarios, by drug or by outcome of interest, in the decisions made for design and analyses in scenarios using large healthcare databases. The most consistent choice was the cohort study design but variability in the other critical decisions was common.


There is great variation among epidemiologists in the design and analytical choices that they make when implementing analyses in observational healthcare databases. These findings confirm that it will be important to generate empiric evidence to inform these decisions and to promote a better understanding of the impact of standardization on research implementation.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others


  1. Strom B, editor. Pharmacoepidemiology. 4th ed. Sussex: John Wiley & Sons, Ltd; 2005.

  2. Schneeweiss S, Avorn J. A review of uses of health care utilization databases for epidemiologic research on therapeutics. J Clin Epidemiol. 2005;58(4):323–37.

    Article  PubMed  Google Scholar 

  3. Farrington CP, Nash J, Miller E. Case series analysis of adverse reactions to vaccines: a comparative evaluation. Am J Epidemiol. 1996;143(11):1165–73.

    Article  PubMed  CAS  Google Scholar 

  4. Wessinger S, Kaplan M, Choi L, Williams M, Lau C, Sharp L, et al. Increased use of selective serotonin reuptake inhibitors in patients admitted with gastrointestinal haemorrhage: a multicentre retrospective analysis. Aliment Pharmacol Ther. 2006;23(7):937–44.

    Article  PubMed  CAS  Google Scholar 

  5. Guyatt GH, Sackett DL, Sinclair JC, Hayward R, Cook DJ, Cook RJ. Users’ guides to the medical literature. IX. A method for grading health care recommendations. Evidence-Based Medicine Working Group. J Am Med Assoc. 1995;274(22):1800–4.

    Article  CAS  Google Scholar 

  6. Ryan PB, Madigan D, Stang PE, Marc Overhage J, Racoosin JA, Hartzema AG, et al. 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.

    Article  PubMed  Google Scholar 

  7. 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 Outcome Res Med. 2012;3(1):e37–44.

    Article  Google Scholar 

  8. 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

  9. Trifiro 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.

    Article  PubMed  Google Scholar 

  10. Madigan DB, Ryan PB, Schuemie MJ. Does design matter? Systematic evaluation of the impact of analytical choices on effect estimates in observational studies. Ther Adv Drug Saf. 2013;4(2):53–62.

    Article  Google Scholar 

  11. 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

  12. Reich CG, Ryan PB, Schuemie MJ. Alternative outcome definitions and their effect on the performance of methods for observational outcome studies. Drug Saf (in this supplement issue). doi:10.1007/s40264-013-0111-1

  13. Gould AL. Accounting for multiplicity in the evaluation of “signals” obtained by data mining from spontaneous report adverse event databases. Biometrical J Biometrische Zeitschrift. 2007;49(1):151–65.

    Article  Google Scholar 

  14. 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

  15. Norén GN, Bergvall T, Ryan PB, Juhlin K, Schuemie MJ, Madigan 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

  16. 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

  17. 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

  18. 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

  19. Ryan PB, Schuemie MJ, Madigan D. Empirical performance of the 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

  20. DuMouchel W, 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

  21. Hernandez-Diaz S, Varas-Lorenzo C, Garcia Rodriguez LA. Non-steroidal antiinflammatory drugs and the risk of acute myocardial infarction. Basic Clin Pharmacol Toxicol. 2006;98(3):266–74.

    Article  PubMed  CAS  Google Scholar 

  22. Loke YK, Kwok CS, Singh S. Comparative cardiovascular effects of thiazolidinediones: systematic review and meta-analysis of observational studies. BMJ. 2011;342:d1309.

    Article  PubMed  Google Scholar 

  23. Green J, Czanner G, Reeves G, Watson J, Wise L, Beral V. Oral bisphosphonates and risk of cancer of oesophagus, stomach, and colorectum: case-control analysis within a UK primary care cohort. BMJ. 2010;341:c4444.

    Article  PubMed  Google Scholar 

  24. Cardwell CR, Abnet CC, Cantwell MM, Murray LJ. Exposure to oral bisphosphonates and risk of esophageal cancer. J Am Med Assoc. 2010;304(6):657–63.

    Article  CAS  Google Scholar 

  25. The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Guide on Methodological Standards in Pharmacoepidemiology (Revision 1). EMA/95098/2010 (cited 2013 January 23). Available from:

  26. Gagne JJ, Nelson JC, Fireman B, Seeger JD, Toh D, Gerhard T, et al. Taxonomy for monitoring methods within a medical product safety surveillance system: year two report of the Mini-Sentinel Taxonomy Project Workgroup (Workgroup) 2012 (cited 2012 October 29). Available from:

  27. Vessey MP, Doll R. Investigation of relation between use of oral contraceptives and thromboembolic disease. Br Med J. 1968;2(5599):199–205.

    Article  PubMed  CAS  Google Scholar 

  28. Vessey M. Learning how to control biases in studies to identify adverse effects of drugs. J R Soc Med. 2007;100(11):526–7.

    Article  PubMed  Google Scholar 

  29. Jick H, Vessey MP. Case-control studies in the evaluation of drug-induced illness. Am J Epidemiol. 1978;107(1):1–7.

    PubMed  CAS  Google Scholar 

  30. Hernán MA, Alonso A, Logan R, Grodstein F, Michels KB, Willett WC, et al. Observational studies analyzed like randomized experiments: an application to postmenopausal hormone therapy and coronary heart disease. Epidemiology. 2008;19(6):766–79.

    Article  PubMed  Google Scholar 

  31. Perrio M, Waller PC, Shakir SA. An analysis of the exclusion criteria used in observational pharmacoepidemiological studies. Pharmacoepidemiol Drug saf. 2007;16(3):329–36.

    Article  PubMed  Google Scholar 

Download references


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., Bioden 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. Stang, Schuemie and Ryan 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 and has become an employee of Janssen Research & Development since completing this research.

Drs. Schuemie and Madigan have received a grant previously from FNIH. J. Marc Overhage, Abraham G. Hartzema, and Emily Welebob have no conflicts of interest to declare.

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.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Paul E. Stang.

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.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 66 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Stang, P.E., Ryan, P.B., Overhage, J.M. et al. Variation in Choice of Study Design: Findings from the Epidemiology Design Decision Inventory and Evaluation (EDDIE) Survey. Drug Saf 36 (Suppl 1), 15–25 (2013).

Download citation

  • Published:

  • Issue Date:

  • DOI: