Current Epidemiology Reports

, Volume 4, Issue 4, pp 262–265 | Cite as

Pharmacoepidemiology in the Era of Real-World Evidence

  • Sengwee TohEmail author
Invited Commentary

Long before the terms real-world data (RWD) and real-world evidence (RWE) were coined, researchers had been using data collected as part of routine healthcare delivery to generate evidence about the utilization, benefits, and risks of medical products [1, 2, 3, 4]. There are several variations to the definitions of RWD and RWE, but most are similar to the ones used by the US Food and Drug Administration (FDA), which defines RWD as “data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources” and RWE as “the clinical evidence regarding the usage, and potential benefits or risks, of a medical product derived from analysis of RWD” [5].

It is not uncommon to re-label an existing construct with a more contemporary descriptor. The term RWD provides a more unified framework to broadly capture the various types of data collected outside of traditional randomized controlled trials. Examples of RWD include electronic health record data,...



Dr. Toh is partially supported by the National Institute of Biomedical Imaging and Bioengineering (U01EB023683).

Compliance with Ethical Standards

Conflict of Interest

Sengwee Toh is a Section Editor for Current Epidemiology Reports.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by the author.


  1. 1.
    Dunlop DM. Drug control and the British Health Service. Ann Intern Med. 1969;71(2):237–44.CrossRefPubMedGoogle Scholar
  2. 2.
    Stolley PD, Lasagna L. Prescribing patterns of physicians. J Chronic Dis. 1969;22(6):395–405.CrossRefPubMedGoogle Scholar
  3. 3.
    Federspiel CF, Ray WA, Schaffner W. Medicaid records as a valid data source: the Tennessee experience. Med Care. 1976;14(2):166–72.CrossRefPubMedGoogle Scholar
  4. 4.
    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. Scholar
  5. 5.
    U.S. Food and Drug Administration. Use of real-world evidence to support regulatory decision-making for medical devices. Guidance for industry and Food and Drug Administration staff. 2017. Available from: Accessed 1 Oct 2017.
  6. 6.
    114th Congress. Public Law 114–255. 2016. Available from: Accessed 1 Oct 2017.
  7. 7.
    European Medicines Agency. Guidance for companies considering the adaptive pathways approach. 2016. Available from: Accessed 1 Oct 2017.
  8. 8.
    Sherman RE, Anderson SA, Dal Pan GJ, Gray GW, Gross T, Hunter NL, et al. Real-world evidence—what is it and what can it tell us? N Engl J Med. 2016;375(23):2293–7. Scholar
  9. 9.
    Jarow JP, LaVange L, Woodcock J. Multidimensional evidence generation and FDA regulatory decision making: defining and using “real-world” data. JAMA. 2017;318(8):703–4. CrossRefPubMedGoogle Scholar
  10. 10.
    Ball R, Robb M, Anderson SA, Dal Pan G. The FDA’s sentinel initiative—a comprehensive approach to medical product surveillance. Clin Pharmacol Ther. 2016;99(3):265–8. Scholar
  11. 11.
    U.S. Food and Drug Administration. Guidance for industry and FDA staff: best practices for conducting and reporting pharmacoepidemiologic safety studies using electronic healthcare data. 2013. Available from: Accessed 1 Oct 2017.
  12. 12.
    The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Guide on methodological standards in pharmacoepidemiology (Revision 5). 2016. Available from: Accessed 1 Oct 2017.
  13. 13.
    Public Policy Committee of the International Society for Pharmacoepidemiology. Guidelines for good pharmacoepidemiology practice (GPP). Pharmacoepidemiol Drug Saf. 2016;25(1):2–10. Scholar
  14. 14.
    Benchimol EI, Smeeth L, Guttmann A, Harron K, Moher D, Petersen I, et al. The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement. PLoS Med. 2015;12(10):e1001885. Scholar
  15. 15.
    Wang SV, Schneeweiss S, Berger ML, Brown J, de Vries F, Douglas I, et al. Reporting to improve reproducibility and facilitate validity assessment for healthcare database studies V1.0. Pharmacoepidemiol Drug Saf. 2017;26(9):1018–32. Scholar
  16. 16.
    Suissa S, Garbe E. Primer: administrative health databases in observational studies of drug effects—advantages and disadvantages. Nat Clin Pract Rheumatol. 2007;3(12):725–32. Scholar
  17. 17.
    Hersh WR, Weiner MG, Embi PJ, Logan JR, Payne PR, Bernstam EV, et al. Caveats for the use of operational electronic health record data in comparative effectiveness research. Med Care. 2013;51(8 Suppl 3):S30–7. Scholar
  18. 18.
    Brown JS, Kahn M, Toh S. Data quality assessment for comparative effectiveness research in distributed data networks. Med Care. 2013;51(8 Suppl 3):S22–9. Scholar
  19. 19.
    Kahn MG, Raebel MA, Glanz JM, Riedlinger K, Steiner JF. A pragmatic framework for single-site and multisite data quality assessment in electronic health record-based clinical research. Med Care. 2012;50(Suppl):S21–9. Scholar
  20. 20.
    Collins FS, Varmus H. A new initiative on precision medicine. N Engl J Med. 2015;372(9):793–5. Scholar
  21. 21.
    Brown JS, Holmes JH, Shah K, Hall K, Lazarus R, Platt R. Distributed health data networks: a practical and preferred approach to multi-institutional evaluations of comparative effectiveness, safety, and quality of care. Med Care. 2010;48(6 Suppl):S45–51. Scholar
  22. 22.
    Curtis LH, Brown J, Platt R. Four health data networks illustrate the potential for a shared national multipurpose big-data network. Health Aff (Millwood). 2014;33(7):1178–86. Scholar
  23. 23.
    Suissa S, Henry D, Caetano P, Dormuth CR, Ernst P, Hemmelgarn B, et al. CNODES: the Canadian Network for Observational Drug Effect Studies. Open Med. 2012;6(4):e134–40.PubMedPubMedCentralGoogle Scholar
  24. 24.
    Fleurence RL, Curtis LH, Califf RM, Platt R, Selby JV, Brown JS. Launching PCORnet, a national patient-centered clinical research network. J Am Med Inform Assoc. 2014;21(4):578–82. Scholar
  25. 25.
    Richesson RL, Hammond WE, Nahm M, Wixted D, Simon GE, Robinson JG, et al. Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory. J Am Med Inform Assoc. 2013;20(e2):e226–31. Scholar
  26. 26.
    Vogel J, Brown JS, Land T, Platt R, Klompas M. MDPHnet: secure, distributed sharing of electronic health record data for public health surveillance, evaluation, and planning. Am J Public Health. 2014;104(12):2265–70. Scholar
  27. 27.
    Toh S, Gagne JJ, Rassen JA, Fireman BH, Kulldorff M, Brown JS. Confounding adjustment in comparative effectiveness research conducted within distributed research networks. Med Care. 2013;51(8 Suppl 3):S4–10. Scholar
  28. 28.
    Gagne JJ, Han X, Hennessy S, Leonard CE, Chrischilles EA, Carnahan RM, et al. Successful comparison of US Food and Drug Administration Sentinel analysis tools to traditional approaches in quantifying a known drug-adverse event association. Clin Pharmacol Ther. 2016;
  29. 29.
    Zhou M, Wang SV, Leonard CE, Gagne JJ, Fuller C, Hampp C, et al. Sentinel modular program for propensity score-matched cohort analyses: application to glyburide, glipizide, and serious hypoglycemia. Epidemiology. 2017;28(6):838–46. Scholar
  30. 30.
    Lavallee DC, Chenok KE, Love RM, Petersen C, Holve E, Segal CD, et al. Incorporating patient-reported outcomes into health care to engage patients and enhance care. Health Aff (Millwood). 2016;35(4):575–82. Scholar
  31. 31.
    AMCP Task Force on Biosimilar Collective Intelligence Systems, Baldziki M, Brown J, Chan H, Cheetham TC, Conn T, et al. Utilizing data consortia to monitor safety and effectiveness of biosimilars and their innovator products. J Manag Care Spec Pharm. 2015;21(1):23–34.  10.18553/jmcp.2015.21.1.23.CrossRefGoogle Scholar
  32. 32.
    Huang SS, Septimus E, Kleinman K, Moody J, Hickok J, Avery TR, et al. Targeted versus universal decolonization to prevent ICU infection. N Engl J Med. 2013;368(24):2255–65. Scholar
  33. 33.
    Hernandez AF, Fleurence RL, Rothman RL. The ADAPTABLE Trial and PCORnet: shining light on a new research paradigm. Ann Intern Med. 2015;163(8):635–6. Scholar
  34. 34.
    Walker AM, Zhou X, Ananthakrishnan AN, Weiss LS, Shen R, Sobel RE, et al. Computer-assisted expert case definition in electronic health records. Int J Med Inform. 2016;86:62–70. Scholar
  35. 35.
    Schuemie MJ, Sen E, t Jong GW, van Soest EM, Sturkenboom MC, Kors JA. Automating classification of free-text electronic health records for epidemiological studies. Pharmacoepidemiol Drug Saf. 2012;21(6):651–8. Scholar
  36. 36.
    Lee BK, Lessler J, Stuart EA. Improving propensity score weighting using machine learning. Stat Med. 2010;29(3):337–46. PubMedPubMedCentralGoogle Scholar
  37. 37.
    Westreich D, Lessler J, Funk MJ. Propensity score estimation: neural networks, support vector machines, decision trees (CART), and meta-classifiers as alternatives to logistic regression. J Clin Epidemiol. 2010;63(8):826–33. Scholar
  38. 38.
    Reade S, Spencer K, Sergeant JC, Sperrin M, Schultz DM, Ainsworth J, et al. Cloudy with a chance of pain: engagement and subsequent attrition of daily data entry in a smartphone pilot study tracking weather, disease severity, and physical activity in patients with rheumatoid arthritis. JMIR Mhealth Uhealth. 2017;5(3):e37. Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Population MedicineHarvard Medical School and Harvard Pilgrim Health Care InstituteBostonUSA

Personalised recommendations