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European Journal of Epidemiology

, Volume 34, Issue 8, pp 719–722 | Cite as

Extending inferences from a randomized trial to a target population

  • Issa J. DahabrehEmail author
  • Miguel A. Hernán
COMMENTARY

In this issue, Weiss discusses “generalizing” inferences from randomized trials to other populations [1]. However, he does not explicitly define what “generalizing” means, assumes that “generalizing” the results of a randomized trial has a single goal, and reduces generalizability to a binary subjective judgment—findings are either generalizable or not generalizable. A growing literature (e.g.,  [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13])  precisely defines the several meanings and goals of extending inferences from randomized trials to another population, and describes analyses whose findings go beyond simple binary judgements. Here, we provide a non-technical overview of this literature. First, we briefly review the main concepts, then we outline the available study designs and statistical approaches.

What do we mean by extending inferences from randomized trials?

We can summarize the goals of extending inferences from randomized trials as learning about counterfactual quantities...

Notes

Funding

This work was supported in part by Patient-Centered Outcomes Research Institute (PCORI) Methods Research Award ME-1502-27794 (Dahabreh) and National Institutes of Health (NIH) Grant R37 AI102634 (Hernán). Statements in this paper do not necessarily represent the views of the PCORI, its Board of Governors, the PCORI Methodology Committee, or the NIH.

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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Health Services Policy and Practice, Center for Evidence Synthesis in Health, School of Public HealthBrown UniversityProvidenceUSA
  2. 2.Department of Epidemiology, School of Public HealthBrown UniversityProvidenceUSA
  3. 3.Department of Epidemiology, Harvard T.H. Chan School of Public HealthHarvard UniversityBostonUSA
  4. 4.Department of Biostatistics, Harvard T.H. Chan School of Public HealthHarvard UniversityBostonUSA
  5. 5.Harvard-MIT Division of Health Sciences and TechnologyBostonUSA

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