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External Validity in Distributed Data Networks
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  • Michael Webster-Clark,
  • Sengwee Toh,
  • Jonathan Arnold,
  • Kathleen M. McTigue,
  • Thomas Carton,
  • Robert Platt
Michael Webster-Clark
McGill University Department of Epidemiology Biostatistics and Occupational Health

Corresponding Author:[email protected]

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Sengwee Toh
Harvard Medical School Department of Population Medicine
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Jonathan Arnold
University of Pittsburgh Department of Medicine
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Kathleen M. McTigue
University of Pittsburgh Department of Medicine
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Thomas Carton
Louisiana Public Health Institute
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Robert Platt
McGill University Department of Epidemiology Biostatistics and Occupational Health
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Purpose: While much has been written about how distributed networks address internal validity, external validity is rarely discussed. We aimed to define key terms related to external validity, discuss how they relate to distributed networks, and identify how three networks (the US Food and Drug Administration’s Sentinel System, the Canadian Network for Observational Drug Effect Studies [CNODES], and PCORnet, the National Patient Centered Clinical Research Network, initiated and supported by the Patient-Centered Outcomes Research Institute. Methods: We define external validity, target populations, target validity, generalizability, and transportability and describe how each relates to distributed networks. We then describe Sentinel, CNODES, and PCORnet and how each approaches these concepts. Results: Each network approaches external validity differently Sentinel answers regulatory questions in the general US population using data from commercial health plans and Medicare fee-for-service beneficiaries and considers external validity when exploring outliers or performing subgroup analyses to examine potential heterogeneity of treatment effects. CNODES focuses on a Canadian target population but includes UK and US data and thus has to make decisions about which partners can be included in each analysis. PCORnet supports a wider array of studies including randomized trials and often assesses whether a given study will be representative of the wider US population. Conclusions: There is no one-size-fits-all approach to external validity within distributed networks. With these networks and comparisons between their findings becoming a key part of pharmacoepidemiology, there is a need to adapt tools for improving external validity to the distributed network setting.
06 Dec 2022Submitted to Pharmacoepidemiology and Drug Safety
06 Dec 2022Submission Checks Completed
06 Dec 2022Assigned to Editor
06 Dec 2022Review(s) Completed, Editorial Evaluation Pending
18 Jan 2023Reviewer(s) Assigned
08 Mar 2023Editorial Decision: Revise Major
22 May 20231st Revision Received
22 May 2023Submission Checks Completed
22 May 2023Assigned to Editor
22 May 2023Review(s) Completed, Editorial Evaluation Pending
26 May 2023Reviewer(s) Assigned
05 Jul 2023Editorial Decision: Accept