Extending inferences from a randomized trial to a new target population

Issa J. Dahabreh, Sarah E. Robertson, Jon A. Steingrimsson, Elizabeth A. Stuart, Miguel A. Hernán

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


When treatment effect modifiers influence the decision to participate in a randomized trial, the average treatment effect in the population represented by the randomized individuals will differ from the effect in other populations. In this tutorial, we consider methods for extending causal inferences about time-fixed treatments from a trial to a new target population of nonparticipants, using data from a completed randomized trial and baseline covariate data from a sample from the target population. We examine methods based on modeling the expectation of the outcome, the probability of participation, or both (doubly robust). We compare the methods in a simulation study and show how they can be implemented in software. We apply the methods to a randomized trial nested within a cohort of trial-eligible patients to compare coronary artery surgery plus medical therapy versus medical therapy alone for patients with chronic coronary artery disease. We conclude by discussing issues that arise when using the methods in applied analyses.

Original languageEnglish (US)
Pages (from-to)1999-2014
Number of pages16
JournalStatistics in Medicine
Issue number14
StatePublished - Jun 30 2020


  • double robustness
  • generalizability
  • observational analyses
  • randomized trials
  • transportability

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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