Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals

Issa J. Dahabreh, Sarah E. Robertson, Eric J. Tchetgen, Elizabeth A. Stuart, Miguel A. Hernán

Research output: Contribution to journalArticlepeer-review

16 Scopus citations


We consider methods for causal inference in randomized trials nested within cohorts of trial-eligible individuals, including those who are not randomized. We show how baseline covariate data from the entire cohort, and treatment and outcome data only from randomized individuals, can be used to identify potential (counterfactual) outcome means and average treatment effects in the target population of all eligible individuals. We review identifiability conditions, propose estimators, and assess the estimators' finite-sample performance in simulation studies. As an illustration, we apply the estimators in a trial nested within a cohort of trial-eligible individuals to compare coronary artery bypass grafting surgery plus medical therapy vs. medical therapy alone for chronic coronary artery disease.

Original languageEnglish (US)
Pages (from-to)685-694
Number of pages10
Issue number2
StatePublished - 2019


  • causal inference
  • clinical trials
  • double robustness
  • generalizability
  • observational studies
  • transportability

ASJC Scopus subject areas

  • Statistics and Probability
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology
  • General Agricultural and Biological Sciences
  • Applied Mathematics


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