Pharmacoepidemiology in the Era of Real-World Evidence
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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” .
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.
- 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: https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm513027.pdf. Accessed 1 Oct 2017.
- 6.114th Congress. Public Law 114–255. 2016. Available from: https://www.congress.gov/114/plaws/publ255/PLAW-114publ255.pdf. Accessed 1 Oct 2017.
- 7.European Medicines Agency. Guidance for companies considering the adaptive pathways approach. 2016. Available from: http://www.ema.europa.eu/docs/en_GB/document_library/Regulatory_and_procedural_guideline/2015/11/WC500196726.pdf. Accessed 1 Oct 2017.
- 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: https://www.fda.gov/downloads/drugs/guidances/ucm243537.pdf. Accessed 1 Oct 2017.
- 12.The European Network of Centres for Pharmacoepidemiology and Pharmacovigilance (ENCePP). Guide on methodological standards in pharmacoepidemiology (Revision 5). 2016. Available from: http://www.encepp.eu/standards_and_guidances/documents/ENCePPGuideofMethStandardsinPE_Rev5.pdf. Accessed 1 Oct 2017.
- 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. https://doi.org/10.1002/pds.4295.CrossRefPubMedPubMedCentralGoogle Scholar
- 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. https://doi.org/10.1097/MLR.0b013e3181d9919f.CrossRefPubMedGoogle Scholar
- 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. https://doi.org/10.1136/amiajnl-2013-001926.CrossRefPubMedPubMedCentralGoogle Scholar
- 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; https://doi.org/10.1002/cpt.429.
- 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. https://doi.org/10.1097/EDE.0000000000000709.CrossRefPubMedGoogle Scholar
- 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
- 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. https://doi.org/10.1016/j.jclinepi.2009.11.020.CrossRefPubMedPubMedCentralGoogle Scholar
- 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. https://doi.org/10.2196/mhealth.6496.CrossRefPubMedPubMedCentralGoogle Scholar