  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.
